Kathleen Mckeown

Also published as: Kathleen McKeown, Kathleen R. McKeown, Kathy McKeown


2021

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A Unified Feature Representation for Lexical Connotations
Emily Allaway | Kathleen McKeown
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Ideological attitudes and stance are often expressed through subtle meanings of words and phrases. Understanding these connotations is critical to recognizing the cultural and emotional perspectives of the speaker. In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives. Our analysis shows that it aligns well with human judgments. Additionally, we present a method for creating lexical representations that capture connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited.

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Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings
Kailash Karthik Saravanakumar | Miguel Ballesteros | Muthu Kumar Chandrasekaran | Kathleen McKeown
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these multiple representations and makes the clustering decision using a neural classifier. The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective. We show that the use of a suitable fine-tuning objective and external knowledge in pre-trained transformer models yields significant improvements in the effectiveness of contextual embeddings for clustering. Our model achieves a new state-of-the-art on a standard stream clustering dataset of English documents.

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Entity-level Factual Consistency of Abstractive Text Summarization
Feng Nan | Ramesh Nallapati | Zhiguo Wang | Cicero Nogueira dos Santos | Henghui Zhu | Dejiao Zhang | Kathleen McKeown | Bing Xiang
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating names of entities that are not present in the source document. We propose a set of new metrics to quantify the entity-level factual consistency of generated summaries and we show that the entity hallucination problem can be alleviated by simply filtering the training data. In addition, we propose a summary-worthy entity classification task to the training process as well as a joint entity and summary generation approach, which yield further improvements in entity level metrics.

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Segmenting Subtitles for Correcting ASR Segmentation Errors
David Wan | Chris Kedzie | Faisal Ladhak | Elsbeth Turcan | Petra Galuscakova | Elena Zotkina | Zhengping Jiang | Peter Bell | Kathleen McKeown
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation. In this work, we propose a model for correcting the acoustic segmentation of ASR models for low-resource languages to improve performance on downstream tasks. We propose the use of subtitles as a proxy dataset for correcting ASR acoustic segmentation, creating synthetic acoustic utterances by modeling common error modes. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance on MT and audio-document cross-language information retrieval (CLIR).

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Emotion-Infused Models for Explainable Psychological Stress Detection
Elsbeth Turcan | Smaranda Muresan | Kathleen McKeown
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.

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Adversarial Learning for Zero-Shot Stance Detection on Social Media
Emily Allaway | Malavika Srikanth | Kathleen McKeown
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.

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Supporting Clustering with Contrastive Learning
Dejiao Zhang | Feng Nan | Xiaokai Wei | Shang-Wen Li | Henghui Zhu | Kathleen McKeown | Ramesh Nallapati | Andrew O. Arnold | Bing Xiang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often overlap with each other in the representation space at the beginning of the learning process, which poses a significant challenge for distance-based clustering in achieving good separation between different categories. To this end, we propose Supporting Clustering with Contrastive Learning (SCCL) – a novel framework to leverage contrastive learning to promote better separation. We assess the performance of SCCL on short text clustering and show that SCCL significantly advances the state-of-the-art results on most benchmark datasets with 3%-11% improvement on Accuracy and 4%-15% improvement on Normalized Mutual Information. Furthermore, our quantitative analysis demonstrates the effectiveness of SCCL in leveraging the strengths of both bottom-up instance discrimination and top-down clustering to achieve better intra-cluster and inter-cluster distances when evaluated with the ground truth cluster labels.

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InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
Yi Fung | Christopher Thomas | Revanth Gangi Reddy | Sandeep Polisetty | Heng Ji | Shih-Fu Chang | Kathleen McKeown | Mohit Bansal | Avi Sil
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

To defend against machine-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% accuracy gain), and more critically, yields fine-grained explanations.

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Cross-language Sentence Selection via Data Augmentation and Rationale Training
Yanda Chen | Chris Kedzie | Suraj Nair | Petra Galuscakova | Rui Zhang | Douglas Oard | Kathleen McKeown
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.

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Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan | Cicero Nogueira dos Santos | Henghui Zhu | Patrick Ng | Kathleen McKeown | Ramesh Nallapati | Dejiao Zhang | Zhiguo Wang | Andrew O. Arnold | Bing Xiang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. Through extensive experiments, we confirm that our method is effective in improving factual consistency and even overall quality of the summaries, as judged by both automatic metrics and human evaluation.

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Event-Centric Natural Language Processing
Muhao Chen | Hongming Zhang | Qiang Ning | Manling Li | Heng Ji | Kathleen McKeown | Dan Roth
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts

This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations of events, (ii) various methods for automated extraction, conceptualization and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLU and commonsense understanding tasks that benefit from aforementioned techniques. We will conclude the tutorial by outlining emerging research problems in this area.

2020

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Event-Guided Denoising for Multilingual Relation Learning
Amith Ananthram | Emily Allaway | Kathleen McKeown
Proceedings of the 28th International Conference on Computational Linguistics

General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus – the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).

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Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages
Efsun Sarioglu Kayi | Linyong Nan | Bohan Qu | Mona Diab | Kathleen McKeown
Proceedings of the 28th International Conference on Computational Linguistics

We release an urgency dataset that consists of English tweets relating to natural crises, along with annotations of their corresponding urgency status. Additionally, we release evaluation datasets for two low-resource languages, i.e. Sinhala and Odia, and demonstrate an effective zero-shot transfer from English to these two languages by training cross-lingual classifiers. We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train classifiers of different architectures on the extracted features. We also explore semi-supervised approaches by utilizing unlabeled tweets and experiment with ensembling different classifiers. With very limited amounts of labeled data in English and zero data in the low resource languages, we show a successful framework of training monolingual and cross-lingual classifiers using deep learning methods which are known to be data hungry. Specifically, we show that the recent deep contextual embeddings are also helpful when dealing with very small-scale datasets. Classifiers that incorporate RoBERTa yield the best performance for English urgency detection task, with F1 scores that are more than 25 points over our baseline classifier. For the zero-shot transfer to low resource languages, classifiers that use LASER features perform the best for Sinhala transfer while XLM-R features benefit the Odia transfer the most.

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Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Dejiao Zhang | Ramesh Nallapati | Henghui Zhu | Feng Nan | Cicero Nogueira dos Santos | Kathleen McKeown | Bing Xiang
Findings of the Association for Computational Linguistics: EMNLP 2020

Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent theoretical work (Zhang et al., 2019b) and adopt the Margin Disparity Discrepancy (MDD) unsupervised domain adaptation algorithm to solve the cross-lingual text labeling problems. Experiments on cross-lingual document classification and NER demonstrate the proposed domain adaptation approach advances the state-of-the-art results by a large margin. Specifically, we improve MDD by efficiently optimizing the margin loss on the source domain via Virtual Adversarial Training (VAT). This bridges the gap between theory and the loss function used in the original work Zhang et al.(2019b), and thereby significantly boosts the performance. Our numerical results also indicate that VAT can remarkably improve the generalization performance of both domains for various domain adaptation approaches.

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WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
Faisal Ladhak | Esin Durmus | Claire Cardie | Kathleen McKeown
Findings of the Association for Computational Linguistics: EMNLP 2020

We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.

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Towards Augmenting Lexical Resources for Slang and African American English
Alyssa Hwang | William R. Frey | Kathleen McKeown
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects

Researchers in natural language processing have developed large, robust resources for understanding formal Standard American English (SAE), but we lack similar resources for variations of English, such as slang and African American English (AAE). In this work, we use word embeddings and clustering algorithms to group semantically similar words in three datasets, two of which contain high incidence of slang and AAE. Since high-quality clusters would contain related words, we could also infer the meaning of an unfamiliar word based on the meanings of words clustered with it. After clustering, we compute precision and recall scores using WordNet and ConceptNet as gold standards and show that these scores are unimportant when the given resources do not fully represent slang and AAE. Amazon Mechanical Turk and expert evaluations show that clusters with low precision can still be considered high quality, and we propose the new Cluster Split Score as a metric for machine-generated clusters. These contributions emphasize the gap in natural language processing research for variations of English and motivate further work to close it.

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Exploring Content Selection in Summarization of Novel Chapters
Faisal Ladhak | Bryan Li | Yaser Al-Onaizan | Kathleen McKeown
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis.

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Incorporating Terminology Constraints in Automatic Post-Editing
David Wan | Chris Kedzie | Faisal Ladhak | Marine Carpuat | Kathleen McKeown
Proceedings of the Fifth Conference on Machine Translation

Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches cannot ensure that they will appear in the final translation. In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95% of the terminologies and also improves translation quality on English-German benchmarks. Even when applied to lexically constrained MT output, our approach is able to improve preservation of the terminologies. However, we show that our models do not learn to copy constraints systematically and suggest a simple data augmentation technique that leads to improved performance and robustness.

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Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies
Chris Kedzie | Kathleen McKeown
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We study the degree to which neural sequence-to-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation. Using two task-oriented dialogue generation benchmarks, we systematically compare the effect of four input linearization strategies on controllability and faithfulness. Additionally, we evaluate how a phrase-based data augmentation method can improve performance. We find that properly aligning input sequences during training leads to highly controllable generation, both when training from scratch or when fine-tuning a larger pre-trained model. Data augmentation further improves control on difficult, randomly generated utterance plans.

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Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros | Rishita Anubhai | Shuai Wang | Nima Pourdamghani | Yogarshi Vyas | Jie Ma | Parminder Bhatia | Kathleen McKeown | Yaser Al-Onaizan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.

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Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
Emily Allaway | Kathleen McKeown
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.

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Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)
Kathy McKeown | Douglas W. Oard | Elizabeth | Richard Schwartz
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

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Subtitles to Segmentation: Improving Low-Resource Speech-to-TextTranslation Pipelines
David Wan | Zhengping Jiang | Chris Kedzie | Elsbeth Turcan | Peter Bell | Kathy McKeown
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation. ASR output segmentation is crucial, as ASR systems segment the input audio using purely acoustic information and are not guaranteed to output sentence-like segments. Since most MT systems expect sentences as input, feeding in longer unsegmented passages can lead to sub-optimal performance. We explore the feasibility of using datasets of subtitles from TV shows and movies to train better ASR segmentation models. We further incorporate part-of-speech (POS) tag and dependency label information (derived from the unsegmented ASR outputs) into our segmentation model. We show that this noisy syntactic information can improve model accuracy. We evaluate our models intrinsically on segmentation quality and extrinsically on downstream MT performance, as well as downstream tasks including cross-lingual information retrieval (CLIR) tasks and human relevance assessments. Our model shows improved performance on downstream tasks for Lithuanian and Bulgarian.

2019

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AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Tuhin Chakrabarty | Christopher Hidey | Smaranda Muresan | Kathy McKeown | Alyssa Hwang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one’s argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.

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Detecting and Reducing Bias in a High Stakes Domain
Ruiqi Zhong | Yanda Chen | Desmond Patton | Charlotte Selous | Kathy McKeown
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Gang-involved youth in cities such as Chicago sometimes post on social media to express their aggression towards rival gangs and previous research has demonstrated that a deep learning approach can predict aggression and loss in posts. To address the possibility of bias in this sensitive application, we developed an approach to systematically interpret the state of the art model. We found, surprisingly, that it frequently bases its predictions on stop words such as “a” or “on”, an approach that could harm social media users who have no aggressive intentions. To tackle this bias, domain experts annotated the rationales, highlighting words that explain why a tweet is labeled as “aggression”. These new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. Our study shows that in high stake scenarios, accuracy alone cannot guarantee a good system and we need new evaluation methods.

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Automatically Inferring Gender Associations from Language
Serina Chang | Kathy McKeown
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings – one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.

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Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
Elsbeth Turcan | Kathy McKeown
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.

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IMHO Fine-Tuning Improves Claim Detection
Tuhin Chakrabarty | Christopher Hidey | Kathy McKeown
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine-tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.

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Fixed That for You: Generating Contrastive Claims with Semantic Edits
Christopher Hidey | Kathy McKeown
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Understanding contrastive opinions is a key component of argument generation. Central to an argument is the claim, a statement that is in dispute. Generating a counter-argument then requires generating a response in contrast to the main claim of the original argument. To generate contrastive claims, we create a corpus of Reddit comment pairs self-labeled by posters using the acronym FTFY (fixed that for you). We then train neural models on these pairs to edit the original claim and produce a new claim with a different view. We demonstrate significant improvement over a sequence-to-sequence baseline in BLEU score and a human evaluation for fluency, coherence, and contrast.

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A Robust Abstractive System for Cross-Lingual Summarization
Jessica Ouyang | Boya Song | Kathy McKeown
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.

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Identifying therapist conversational actions across diverse psychotherapeutic approaches
Fei-Tzin Lee | Derrick Hull | Jacob Levine | Bonnie Ray | Kathy McKeown
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational “action” each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.

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A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models
Chris Kedzie | Kathleen McKeown
Proceedings of the 12th International Conference on Natural Language Generation

Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks. In order for such models to truly be useful, they must be capable of correctly generating utterances for novel meaning representations (MRs) at test time. In practice, even sophisticated DNNs with various forms of semantic control frequently fail to generate utterances faithful to the input MR. In this paper, we propose an architecture agnostic self-training method to sample novel MR/text utterance pairs to augment the original training data. Remarkably, after training on the augmented data, even simple encoder-decoder models with greedy decoding are capable of generating semantically correct utterances that are as good as state-of-the-art outputs in both automatic and human evaluations of quality.

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Neural Network Alignment for Sentential Paraphrases
Jessica Ouyang | Kathy McKeown
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a monolingual alignment system for long, sentence- or clause-level alignments, and demonstrate that systems designed for word- or short phrase-based alignment are ill-suited for these longer alignments. Our system is capable of aligning semantically similar spans of arbitrary length. We achieve significantly higher recall on aligning phrases of four or more words and outperform state-of-the- art aligners on the long alignments in the MSR RTE corpus.

2018

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Predictive Embeddings for Hate Speech Detection on Twitter
Rohan Kshirsagar | Tyrus Cukuvac | Kathy McKeown | Susan McGregor
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.

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Detecting Gang-Involved Escalation on Social Media Using Context
Serina Chang | Ruiqi Zhong | Ethan Adams | Fei-Tzin Lee | Siddharth Varia | Desmond Patton | William Frey | Chris Kedzie | Kathy McKeown
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.

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Content Selection in Deep Learning Models of Summarization
Chris Kedzie | Kathleen McKeown | Hal Daumé III
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learning models for summarization for those domains that do have massive datasets (i.e., news). At the same time, they suggest important questions for new research in summarization; namely, new forms of sentence representations or external knowledge sources are needed that are better suited to the sumarization task.

2017

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Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum
Christopher Hidey | Elena Musi | Alyssa Hwang | Smaranda Muresan | Kathy McKeown
Proceedings of the 4th Workshop on Argument Mining

Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e.g., claims, premises) and their argumentative relations (e.g., support, attack). Less emphasis has been placed on analyzing the semantic types of argument components. We propose a two-tiered annotation scheme to label claims and premises and their semantic types in an online persuasive forum, Change My View, with the long-term goal of understanding what makes a message persuasive. Premises are annotated with the three types of persuasive modes: ethos, logos, pathos, while claims are labeled as interpretation, evaluation, agreement, or disagreement, the latter two designed to account for the dialogical nature of our corpus. We aim to answer three questions: 1) can humans reliably annotate the semantic types of argument components? 2) are types of premises/claims positioned in recurrent orders? and 3) are certain types of claims and/or premises more likely to appear in persuasive messages than in non-persuasive messages?

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Domain-Adaptable Hybrid Generation of RDF Entity Descriptions
Or Biran | Kathleen McKeown
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity. While they can be useful as a starting point for generating descriptions of entities, they often miss important information about an entity that cannot be captured as simple relations. In addition, generic approaches to generation from RDF cannot capture the unique style and content of specific domains. We describe a framework for hybrid generation of entity descriptions, which combines generation from RDF data with text extracted from a corpus, and extracts unique aspects of the domain from the corpus to create domain-specific generation systems. We show that each component of our approach significantly increases the satisfaction of readers with the text across multiple applications and domains.

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SMARTies: Sentiment Models for Arabic Target entities
Noura Farra | Kathy McKeown
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources. We present a system that is applied to complex posts written in response to Arabic newspaper articles. Our goal is to identify important entity “targets” within the post along with the polarity expressed about each target. We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.

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Crowd-Sourced Iterative Annotation for Narrative Summarization Corpora
Jessica Ouyang | Serina Chang | Kathy McKeown
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present an iterative annotation process for producing aligned, parallel corpora of abstractive and extractive summaries for narrative. Our approach uses a combination of trained annotators and crowd-sourcing, allowing us to elicit human-generated summaries and alignments quickly and at low cost. We use crowd-sourcing to annotate aligned phrases with the text-to-text generation techniques needed to transform each phrase into the other. We apply this process to a corpus of 476 personal narratives, which we make available on the Web.

2016

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Identifying Causal Relations Using Parallel Wikipedia Articles
Christopher Hidey | Kathy McKeown
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Mining Paraphrasal Typed Templates from a Plain Text Corpus
Or Biran | Terra Blevins | Kathleen McKeown
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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An Entity-Focused Approach to Generating Company Descriptions
Gavin Saldanha | Or Biran | Kathleen McKeown | Alfio Gliozzo
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Social Proof: The Impact of Author Traits on Influence Detection
Sara Rosenthal | Kathy McKeown
Proceedings of the First Workshop on NLP and Computational Social Science

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Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression
Terra Blevins | Robert Kwiatkowski | Jamie MacBeth | Kathleen McKeown | Desmond Patton | Owen Rambow
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Violence is a serious problems for cities like Chicago and has been exacerbated by the use of social media by gang-involved youths for taunting rival gangs. We present a corpus of tweets from a young and powerful female gang member and her communicators, which we have annotated with discourse intention, using a deep read to understand how and what triggered conversations to escalate into aggression. We use this corpus to develop a part-of-speech tagger and phrase table for the variant of English that is used and a classifier for identifying tweets that express grieving and aggression.

2015

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System Combination for Machine Translation through Paraphrasing
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Discourse Planning with an N-gram Model of Relations
Or Biran | Kathleen McKeown
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Modeling Reportable Events as Turning Points in Narrative
Jessica Ouyang | Kathleen McKeown
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Annotating Targets of Opinions in Arabic using Crowdsourcing
Noura Farra | Kathy McKeown | Nizar Habash
Proceedings of the Second Workshop on Arabic Natural Language Processing

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PDTB Discourse Parsing as a Tagging Task: The Two Taggers Approach
Or Biran | Kathleen McKeown
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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I Couldn’t Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions
Sara Rosenthal | Kathy McKeown
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Predicting Salient Updates for Disaster Summarization
Chris Kedzie | Kathleen McKeown | Fernando Diaz
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science
Cristian Danescu-Niculescu-Mizil | Jacob Eisenstein | Kathleen McKeown | Noah A. Smith
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

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Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media
Sara Rosenthal | Kathy McKeown | Apoorv Agarwal
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Towards Automatic Detection of Narrative Structure
Jessica Ouyang | Kathy McKeown
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present novel computational experiments using William Labov’s theory of narrative analysis. We describe his six elements of narrative structure and construct a new corpus based on his most recent work on narrative. Using this corpus, we explore the correspondence between Labov’s elements of narrative structure and the implicit discourse relations of the Penn Discourse Treebank, and we construct a mapping between the elements of narrative structure and the discourse relation classes of the PDTB. We present first experiments on detecting Complicating Actions, the most common of the elements of narrative structure, achieving an f-score of 71.55. We compare the contributions of features derived from narrative analysis, such as the length of clauses and the tenses of main verbs, with those of features drawn from work on detecting implicit discourse relations. Finally, we suggest directions for future research on narrative structure, such as applications in assessing text quality and in narrative generation.

2013

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Classifying Taxonomic Relations between Pairs of Wikipedia Articles
Or Biran | Kathleen McKeown
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Cluster-based Web Summarization
Yves Petinot | Kathleen McKeown | Kapil Thadani
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Supervised Sentence Fusion with Single-Stage Inference
Kapil Thadani | Kathleen McKeown
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Semantic Technologies in IBM Watson
Alfio Gliozzo | Or Biran | Siddharth Patwardhan | Kathleen McKeown
Proceedings of the Fourth Workshop on Teaching NLP and CL

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Sentence Compression with Joint Structural Inference
Kapil Thadani | Kathleen McKeown
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

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Aggregated Word Pair Features for Implicit Discourse Relation Disambiguation
Or Biran | Kathleen McKeown
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media
Sara Rosenthal | Kathy McKeown
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Detecting Influencers in Written Online Conversations
Or Biran | Sara Rosenthal | Jacob Andreas | Kathleen McKeown | Owen Rambow
Proceedings of the Second Workshop on Language in Social Media

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Annotating Agreement and Disagreement in Threaded Discussion
Jacob Andreas | Sara Rosenthal | Kathleen McKeown
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We introduce a new corpus of sentence-level agreement and disagreement annotations over LiveJournal and Wikipedia threads. This is the first agreement corpus to offer full-document annotations for threaded discussions. We provide a methodology for coding responses as well as an implemented tool with an interface that facilitates annotation of a specific response while viewing the full context of the thread. Both the results of an annotator questionnaire and high inter-annotator agreement statistics indicate that the annotations collected are of high quality.

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Can Automatic Post-Editing Make MT More Meaningful
Kristen Parton | Nizar Habash | Kathleen McKeown | Gonzalo Iglesias | Adrià de Gispert
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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Phrase-level System Combination for Machine Translation Based on Target-to-Target Decoding
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

In this paper, we propose a novel lattice-based MT combination methodology that we call Target-to-Target Decoding (TTD). The combination process is carried out as a “translation” from backbone to the combination result. This perspective suggests the use of existing phrase-based MT techniques in the combination framework. We show how phrase extraction rules and confidence estimations inspired from machine translation improve results. We also propose system-specific LMs for estimating N-gram consensus. Our results show that our approach yields a strong improvement over the best single MT system and competes with other state-of-the-art combination systems.

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Lost & Found in Translation: Impact of Machine Translated Results on Translingual Information Retrieval
Kristen Parton | Nizar Habash | Kathleen McKeown
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

In an ideal cross-lingual information retrieval (CLIR) system, a user query would generate a search over documents in a different language and the relevant results would be presented in the user’s language. In practice, CLIR systems are typically evaluated by judging result relevance in the document language, to factor out the effects of translating the results using machine translation (MT). In this paper, we investigate the influence of four different approaches for integrating MT and CLIR on both retrieval accuracy and user judgment of relevancy. We create a corpus with relevance judgments for both human and machine translated results, and use it to quantify the effect that MT quality has on end-to-end relevance. We find that MT errors result in a 16-39% decrease in mean average precision over the ground truth system that uses human translations. MT errors also caused relevant sentences to appear irrelevant – 5-19% of sentences were relevant in human translation, but were judged irrelevant in MT. To counter this degradation, we present two hybrid retrieval models and two automatic MT post-editing techniques and show that these approaches substantially mitigate the errors and improve the end-to-end relevance.

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Learning to Automatically Post-Edit Dropped Words in MT
Jacob Mundt | Kristen Parton | Kathleen McKeown
Workshop on Post-Editing Technology and Practice

Automatic post-editors (APEs) can improve adequacy of MT output by detecting and reinserting dropped content words, but the location where these words are inserted is critical. In this paper, we describe a probabilistic approach for learning reinsertion rules for specific languages and MT systems, as well as a method for synthesizing training data from reference translations. We test the insertion logic on MT systems for Chinese to English and Arabic to English. Our adaptive APE is able to insert within 3 words of the best location 73% of the time (32% in the exact location) in Arabic-English MT output, and 67% of the time in Chinese-English output (30% in the exact location), and delivers improved performance on automated adequacy metrics over a previous rule-based approach to insertion. We consider how particular aspects of the insertion problem make it particularly amenable to machine learning solutions.

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Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012)

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Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
Wei-Yun Ma | Kathleen McKeown
International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 4, December 2012-Special Issue on Selected Papers from ROCLING XXIV

2011

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Towards Strict Sentence Intersection: Decoding and Evaluation Strategies
Kapil Thadani | Kathleen McKeown
Proceedings of the Workshop on Monolingual Text-To-Text Generation

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Information Status Distinctions and Referring Expressions: An Empirical Study of References to People in News Summaries
Advaith Siddharthan | Ani Nenkova | Kathleen McKeown
Computational Linguistics, Volume 37, Issue 4 - December 2011

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Identifying Event Descriptions using Co-training with Online News Summaries
William Yang Wang | Kapil Thadani | Kathleen McKeown
Proceedings of 5th International Joint Conference on Natural Language Processing

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System Combination for Machine Translation Based on Text-to-Text Generation
Wei-Yun Ma | Kathleen Mckeown
Proceedings of Machine Translation Summit XIII: Papers

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Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations
Sara Rosenthal | Kathleen McKeown
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Optimal and Syntactically-Informed Decoding for Monolingual Phrase-Based Alignment
Kapil Thadani | Kathleen McKeown
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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A Hierarchical Model of Web Summaries
Yves Petinot | Kathleen McKeown | Kapil Thadani
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Time-Efficient Creation of an Accurate Sentence Fusion Corpus
Kathleen McKeown | Sara Rosenthal | Kapil Thadani | Coleman Moore
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Corpus Creation for New Genres: A Crowdsourced Approach to PP Attachment
Mukund Jha | Jacob Andreas | Kapil Thadani | Sara Rosenthal | Kathleen McKeown
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Tense and Aspect Assignment in Narrative Discourse
David Elson | Kathleen McKeown
Proceedings of the 6th International Natural Language Generation Conference

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Extracting Social Networks from Literary Fiction
David Elson | Nicholas Dames | Kathleen McKeown
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Towards Semi-Automated Annotation for Prepositional Phrase Attachment
Sara Rosenthal | William Lipovsky | Kathleen McKeown | Kapil Thadani | Jacob Andreas
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper investigates whether high-quality annotations for tasks involving semantic disambiguation can be obtained without a major investment in time or expense. We examine the use of untrained human volunteers from Amazons Mechanical Turk in disambiguating prepositional phrase (PP) attachment over sentences drawn from the Wall Street Journal corpus. Our goal is to compare the performance of these crowdsourced judgments to the annotations supplied by trained linguists for the Penn Treebank project in order to indicate the viability of this approach for annotation projects that involve contextual disambiguation. The results of our experiments on a sample of the Wall Street Journal corpus show that invoking majority agreement between multiple human workers can yield PP attachments with fairly high precision. This confirms that a crowdsourcing approach to syntactic annotation holds promise for the generation of training corpora in new domains and genres where high-quality annotations are not available and difficult to obtain.

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Building a Bank of Semantically Encoded Narratives
David K. Elson | Kathleen R. McKeown
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text).

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“Got You!”: Automatic Vandalism Detection in Wikipedia with Web-based Shallow Syntactic-Semantic Modeling
William Yang Wang | Kathleen McKeown
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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MT Error Detection for Cross-Lingual Question Answering
Kristen Parton | Kathleen McKeown
Coling 2010: Posters

2009

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Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task
Kristen Parton | Kathleen R. McKeown | Bob Coyne | Mona T. Diab | Ralph Grishman | Dilek Hakkani-Tür | Mary Harper | Heng Ji | Wei Yun Ma | Adam Meyers | Sara Stolbach | Ang Sun | Gokhan Tur | Wei Xu | Sibel Yaman
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Where’s the Verb? Correcting Machine Translation During Question Answering
Wei-Yun Ma | Kathy McKeown
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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A Tool for Deep Semantic Encoding of Narrative Texts
David K. Elson | Kathleen R. McKeown
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

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Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams
Apoorv Agarwal | Fadi Biadsy | Kathleen R. McKeown
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Query-focused Summarization Using Text-to-Text Generation: When Information Comes from Multilingual Sources
Kathy McKeown
Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+Sum 2009)

2008

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A Framework for Identifying Textual Redundancy
Kapil Thadani | Kathleen McKeown
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Lexicalized Markov Grammars for Sentence Compression
Michel Galley | Kathleen McKeown
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Building and Refining Rhetorical-Semantic Relation Models
Sasha Blair-Goldensohn | Kathleen McKeown | Owen Rambow
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Question Answering Using Integrated Information Retrieval and Information Extraction
Barry Schiffman | Kathleen McKeown | Ralph Grishman | James Allan
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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Automatic Creation of Domain Templates
Elena Filatova | Vasileios Hatzivassiloglou | Kathleen McKeown
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Lessons Learned from Large Scale Evaluation of Systems that Produce Text: Nightmares and Pleasant Surprises
Kathleen R. McKeown
Proceedings of the Fourth International Natural Language Generation Conference

2005

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Improving Multilingual Summarization: Using Redundancy in the Input to Correct MT errors
Advaith Siddharthan | Kathleen McKeown
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Automatically Learning Cognitive Status for Multi-Document Summarization of Newswire
Ani Nenkova | Advaith Siddharthan | Kathleen McKeown
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Context and Learning in Novelty Detection
Barry Schiffman | Kathleen McKeown
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Text Summarization: News and Beyond
Kathy McKeown
Proceedings of the Australasian Language Technology Workshop 2005

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Sentence Fusion for Multidocument News Summarization
Regina Barzilay | Kathleen R. McKeown
Computational Linguistics, Volume 31, Number 3, September 2005

2004

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Columbia Newsblaster: Multilingual News Summarization on the Web
David Kirk Evans | Judith L. Klavans | Kathleen R. McKeown
Demonstration Papers at HLT-NAACL 2004

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Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies
Michel Galley | Kathleen McKeown | Julia Hirschberg | Elizabeth Shriberg
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Generating Overview Summaries of Ongoing Email Thread Discussions
Stephen Wan | Kathy McKeown
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Detection of Question-Answer Pairs in Email Conversations
Lokesh Shrestha | Kathleen McKeown
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Syntactic Simplification for Improving Content Selection in Multi-Document Summarization
Advaith Siddharthan | Ani Nenkova | Kathleen McKeown
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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References to Named Entities: a Corpus Study
Ani Nenkova | Kathleen McKeown
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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Columbia’s Newsblaster: New Features and Future Directions
Kathleen McKeown | Regina Barzilay | John Chen | David Elson | David Evans | Judith Klavans | Ani Nenkova | Barry Schiffman | Sergey Sigelman
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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Discourse Segmentation of Multi-Party Conversation
Michel Galley | Kathleen R. McKeown | Eric Fosler-Lussier | Hongyan Jing
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Statistical Acquisition of Content Selection Rules for Natural Language Generation
Pablo Ariel Duboue | Kathleen R. McKeown
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

2002

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Using the Annotated Bibliography as a Resource for Indicative Summarization
Min-Yen Kan | Judith L. Klavans | Kathleen R. McKeown
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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NLP Found Helpful (at least for one Text Categorization Task)
Carl Sable | Kathleen McKeown | Kenneth Church
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

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Proceedings of the International Natural Language Generation Conference
Kathleen McKeown
Proceedings of the International Natural Language Generation Conference

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Corpus-trained Text Generation for Summarization
Min-Yen Kan | Kathleen R. McKeown
Proceedings of the International Natural Language Generation Conference

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Content Planner Construction via Evolutionary Algorithms and a Corpus-based Fitness Function
Pablo Duboue | Kathleen McKeown
Proceedings of the International Natural Language Generation Conference

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Introduction to the Special Issue on Summarization
Dragomir R. Radev | Eduard Hovy | Kathleen McKeown
Computational Linguistics, Volume 28, Number 4, December 2002

2001

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Extracting Paraphrases from a Parallel Corpus
Regina Barzilay | Kathleen R. McKeown
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

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Empirically Estimating Order Constraints for Content Planning in Generation
Pablo A. Duboue | Kathleen R. McKeown
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

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Applying Natural Language Generation to Indicative Summarization
Min-Yen Kan | Kathleen R. McKeown | Judith L. Klavans
Proceedings of the ACL 2001 Eighth European Workshop on Natural Language Generation (EWNLG)

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Sentence Ordering in Multidocument Summarization
Regina Barzilay | Noemie Elhadad | Kathleen R. McKeown
Proceedings of the First International Conference on Human Language Technology Research

2000

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Experiments in Automated Lexicon Building for Text Searching
Barry Schiffman | Kathleen R. McKeown
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Learning Methods to Combine Linguistic Indicators:Improving Aspectual Classification and Revealing Linguistic Insights
Eric V. Siegel | Kathleen R. McKeown
Computational Linguistics, Volume 26, Number 4, December 2000

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Cut and Paste Based Text Summarization
Hongyan Jing | Kathleen R. McKeown
1st Meeting of the North American Chapter of the Association for Computational Linguistics

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Generating Referring Quantified Expressions
James Shaw | Kathleen McKeown
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

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Integrating a Large-Scale, Reusable Lexicon with a Natural Language Generator
Hongyan Jing | Yael Dahan | Michael Elhadad | Kathy McKeown
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

1999

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Word Informativeness and Automatic Pitch Accent Modeling
Shimei Pan | Kathleen R. McKeown
1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

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Information Fusion in the Context of Multi-Document Summarization
Regina Barzilay | Kathleen R. McKeown | Michael Elhadad
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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Combining Multiple, Large-Scale Resources in a Reusable Lexicon for Natural Language Generation
Hongyan Jing | Kathleen McKeown
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Learning Intonation Rules for Concept to Speech Generation
Shimei Pan | Kathleen McKeown
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Combining Multiple, Large-Scale Resources in a Reusable Lexicon for Natural Language Generation
Hongyan Jing | Kathleen McKeown
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Learning Intonation Rules for Concept to Speech Generation
Shimei Pan | Kathleen McKeown
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Linear Segmentation and Segment Significance
Min-Yen Kan | Judith L. Klavans | Kathleen R. McKeown
Sixth Workshop on Very Large Corpora

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Generating Natural Language Summaries from Multiple On-Line Sources
Dragomir R. Radev | Kathleen R. McKeown
Computational-Linguistics, Volume 24, Number 3, September 1998

1997

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Predicting the Semantic Orientation of Adjectives
Vasileios Hatzivassiloglou | Kathleen R. McKeown
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

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Building a Generation Knowledge Source using Internet-Accessible Newswire
Dragomir R. Radev | Kathleen R. McKeown
Fifth Conference on Applied Natural Language Processing

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Language Generation for Multimedia Healthcare Briefings
Kathleen R. McKeown | Desmond A. Jordan | Shimei Pan | James Shaw | Barry A. Allen
Fifth Conference on Applied Natural Language Processing

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Floating Constraints in Lexical Choice
Michael Elhadad | Kathleen McKeown | Jacques Robin
Computational Linguistics, Volume 23, Number 2, June 1997

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Investigating Complementary Methods for Verb Sense Pruning
Hongyan Jing | Vasileios Hatzivassiloglou | Rebecca Passonneau | Kathleen McKeown
Tagging Text with Lexical Semantics: Why, What, and How?

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Software Re-Use and Evolution in Text Generation Applications
Karen Kukich | Rebecca Passonneau | Kathleen McKeown | Dragomir Radev | Vasileios Hatzivassiloglou | Hongyan Jing
From Research to Commercial Applications: Making NLP Work in Practice

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Integrating Language Generation with Speech Synthesis in a Concept to Speech System
Shimei Pan | Kathleen R. McKeown
Concept to Speech Generation Systems

1996

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Translating Collocations for Bilingual Lexicons: A Statistical Approach
Frank Smadja | Kathleen R. McKeown | Vasileios Hatzivassiloglou
Computational Linguistics, Volume 22, Number 1, March 1996

1995

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A Quantitative Evaluation of Linguistic Tests for the Automatic Prediction of Semantic Markedness
Vasileios Hatzivassiloglou | Kathleen McKeown
33rd Annual Meeting of the Association for Computational Linguistics

1994

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Practical Issues in Automatic Documentation Generation
Kathleen McKeown | Karen Kukich | James Shaw
Fourth Conference on Applied Natural Language Processing

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Aligning Noisy Parallel Corpora Across Language Groups: Word Pair Feature Matching by Dynamic Time Warping
Pascale Fung | Kathleen McKeown
Proceedings of the First Conference of the Association for Machine Translation in the Americas

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Translating Collocations for Use in Bilingual Lexicons
Frank Smadja | Kathleen McKeown
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Extracting Constraints on Word Usage from Large Text Corpora
Kathleen McKeown | Rebecca Passonneau
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Augmenting Lexicons Automatically: Clustering Semantically Related Adjectives
Kathleen McKeown | Vasileios Hatzivassiloglou
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Extracting Constraints on Word Usage from Large Text Corpora
Kathleen McKeown | Rebecca Passonneau
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Towards the Automatic Identification of Adjectival Scales: Clustering Adjectives According to Meaning
Vasileios Hatzivassiloglou | Kathleen R. McKeown
31st Annual Meeting of the Association for Computational Linguistics

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Tailoring Lexical Choice to the User’s Vocabulary in Multimedia Explanation Generation
Kathleen McKeown | Jacques Robin | Michael Tanenblatt
31st Annual Meeting of the Association for Computational Linguistics

1992

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Session 9: Natural Language Processings
Kathleen McKeown
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Extracting Constraints on Word Usage from Large Text Corpora
Kathleen McKeown | Diane Litman | Rebecca Passonneau
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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Interactive Multimedia Explanation for Equipment Maintenance and Repair
Kathleen McKeown | Steven Feiner
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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Automatically Extracting and Representing Collocations for Language Generation
Frank A. Smadja | Kathleen R. McKeown
28th Annual Meeting of the Association for Computational Linguistics

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Generating Connectives
Michael Elhadad | Kathleen R. McKeown
COLING 1990 Volume 3: Papers presented to the 13th International Conference on Computational Linguistics

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Interactive Multimedia Explanation for Equipment Maintenance and Repair
Kathleen McKeown | Steven Feiner
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Interactive Multimedia Explanation for Equipment Maintenance and Repair
Kathleen McKeown | Steven Feiner
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Proceedings of the Fifth International Workshop on Natural Language Generation
Kathleen R. McKeown | Johanna D. Moore | Sergei Nirenburg
Proceedings of the Fifth International Workshop on Natural Language Generation

1989

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Speech Recognition in Parallel
Salvatore J. Stolfo | Zvi Galil | Kathleen McKeown | Russell Mills
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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Coordinating Text and Graphics in Explanation Generation
Steven K. Feiner | Kathleen R. McKeown
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

1987

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Functional Unification Grammar Revisited
Kathleen R. McKeown | Cecile L. Paris
25th Annual Meeting of the Association for Computational Linguistics

1984

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Natural Language for Exert Systems: Comparisons with Database Systems
Kathleen R. McKeown
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

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Using Focus to Generate Complex and Simple Sentences
Marcia A. Derr | Kathleen R. McKeown
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

1983

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Paraphrasing Questions Using Given and new information
Kathleen R. McKeown
American Journal of Computational Linguistics, Volume 9, Number 1, January-March 1983

1982

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The Text System for Natural Language Generation: An Overview
Kathleen R. McKeown
20th Annual Meeting of the Association for Computational Linguistics

1979

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Paraphrasing Using Given and New Information in a Question-Answer System
Kathleen R. McKeown
17th Annual Meeting of the Association for Computational Linguistics

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