Mari Ostendorf

Also published as: M Ostendorf, M. Ostendorf


2023

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One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Hongjin Su | Weijia Shi | Jungo Kasai | Yizhong Wang | Yushi Hu | Mari Ostendorf | Wen-tau Yih | Noah A. Smith | Luke Zettlemoyer | Tao Yu
Findings of the Association for Computational Linguistics: ACL 2023

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io.

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InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions
Zeqiu Wu | Ryu Parish | Hao Cheng | Sewon Min | Prithviraj Ammanabrolu | Mari Ostendorf | Hannaneh Hajishirzi
Transactions of the Association for Computational Linguistics, Volume 11

In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarification, or provides relevant information to address user queries. The data supports two subtasks, evidence passage identification and response generation, as well as a human evaluation protocol to assess model performance. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both systems significantly underperform humans, suggesting ample room for improvement in future studies.1

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Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts
Sitong Zhou | Meliha Yetisgen | Mari Ostendorf
Proceedings of the 5th Clinical Natural Language Processing Workshop

This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better generalization across exam modalities compared to approaches that employ BERT-based task-specific classification layers. We then develop methods that reduce the inference cost of the model, making large-scale corpus processing more feasible for clinical applications. Specifically, we introduce a generative technique that decomposes complex tasks into smaller subtask blocks, which improves a single-pass model when combined with multitask training. In addition, we leverage target-domain contexts during inference to enhance domain adaptation, enabling use of smaller models. Analyses offer insights into the benefits of different cost reduction strategies.

2022

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CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu | Yi Luan | Hannah Rashkin | David Reitter | Hannaneh Hajishirzi | Mari Ostendorf | Gaurav Singh Tomar
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context. Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question. It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-the-shelf retriever. CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers. Our extensive analysis also shows the robustness of CONQRR to out-of-domain dialogues as well as to zero query rewriting supervision.

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In-Context Learning for Few-Shot Dialogue State Tracking
Yushi Hu | Chia-Hsuan Lee | Tianbao Xie | Tao Yu | Noah A. Smith | Mari Ostendorf
Findings of the Association for Computational Linguistics: EMNLP 2022

Collecting and annotating task-oriented dialogues is time-consuming and costly. Thus, zero and few shot learning for dialogue tasks presents an exciting opportunity. In this work, we propose an in-context (IC) learning framework for zero-shot and few-shot learning dialogue state tracking (DST), where a large pretrained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates. This approach is more flexible and scalable than prior DST work when adapting to new domains and scenarios. To better leverage a tabular domain description in the LM prompt, we reformulate DST into a text-to-SQL problem. We also propose a novel approach to retrieve annotated dialogues as exemplars. Empirical results on MultiWOZ show that our method IC-DST substantially outperforms previous fine-tuned state-of-the-art models in few-shot settings. In addition, we test IC-DST in zero-shot settings, in which the model only takes a fixed task instruction as input, finding that it outperforms previous zero-shot methods by a large margin.

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Unsupervised Learning of Hierarchical Conversation Structure
Bo-Ru Lu | Yushi Hu | Hao Cheng | Noah A. Smith | Mari Ostendorf
Findings of the Association for Computational Linguistics: EMNLP 2022

Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization.

2021

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Representations for Question Answering from Documents with Tables and Text
Vicky Zayats | Kristina Toutanova | Mari Ostendorf
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Tables in web documents are pervasive and can be directly used to answer many of the queries searched on the web, motivating their integration in question answering. Very often information presented in tables is succinct and hard to interpret with standard language representations. On the other hand, tables often appear within textual context, such as an article describing the table. Using the information from an article as additional context can potentially enrich table representations. In this work we aim to improve question answering from tables by refining table representations based on information from surrounding text. We also present an effective method to combine text and table-based predictions for question answering from full documents, obtaining significant improvements on the Natural Questions dataset (Kwiatkowski et al., 2019).

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DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Zeqiu Wu | Bo-Ru Lu | Hannaneh Hajishirzi | Mari Ostendorf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.

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Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Chia-Hsuan Lee | Hao Cheng | Mari Ostendorf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.

2019

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Automated Essay Scoring with Discourse-Aware Neural Models
Farah Nadeem | Huy Nguyen | Yang Liu | Mari Ostendorf
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineering, but they typically require more annotated data. This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays. Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.

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Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection
Vicky Zayats | Mari Ostendorf
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)

Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.

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A Dynamic Speaker Model for Conversational Interactions
Hao Cheng | Hao Fang | Mari Ostendorf
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)

Individual differences in speakers are reflected in their language use as well as in their interests and opinions. Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations. In this work, we introduce a neural model for learning a dynamically updated speaker embedding in a conversational context. Initial model training is unsupervised, using context-sensitive language generation as an objective, with the context being the conversation history. Further fine-tuning can leverage task-dependent supervised training. The learned neural representation of speakers is shown to be useful for content ranking in a socialbot and dialog act prediction in human-human conversations.

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A general framework for information extraction using dynamic span graphs
Yi Luan | Dave Wadden | Luheng He | Amy Shah | Mari Ostendorf | Hannaneh Hajishirzi
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 introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types and coreferences. The dynamic span graph allow coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multi-task frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.

2018

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Personalized Language Model for Query Auto-Completion
Aaron Jaech | Mari Ostendorf
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Query auto-completion is a search engine feature whereby the system suggests completed queries as the user types. Recently, the use of a recurrent neural network language model was suggested as a method of generating query completions. We show how an adaptable language model can be used to generate personalized completions and how the model can use online updating to make predictions for users not seen during training. The personalized predictions are significantly better than a baseline that uses no user information.

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Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Yi Luan | Luheng He | Mari Ostendorf | Hannaneh Hajishirzi
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.

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The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings
Yi Luan | Mari Ostendorf | Hannaneh Hajishirzi
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our submission for SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. Our model is based on the end-to-end relation extraction model of (Miwa and Bansal, 2016) with several enhancements such as character-level encoding attention mechanism on selecting pretrained concept candidate embeddings. Our official submission ranked the second in relation classification task (Subtask 1.1 and Subtask 2 Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario 1).

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Estimating Linguistic Complexity for Science Texts
Farah Nadeem | Mari Ostendorf
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

Evaluation of text difficulty is important both for downstream tasks like text simplification, and for supporting educators in classrooms. Existing work on automated text complexity analysis uses linear models with engineered knowledge-driven features as inputs. While this offers interpretability, these models have lower accuracy for shorter texts. Traditional readability metrics have the additional drawback of not generalizing to informational texts such as science. We propose a neural approach, training on science and other informational texts, to mitigate both problems. Our results show that neural methods outperform knowledge-based linear models for short texts, and have the capacity to generalize to genres not present in the training data.

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Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
Trang Tran | Shubham Toshniwal | Mohit Bansal | Kevin Gimpel | Karen Livescu | Mari Ostendorf
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features. We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline. For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.

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Community Member Retrieval on Social Media Using Textual Information
Aaron Jaech | Shobhit Hathi | Mari Ostendorf
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive labeled examples defines the community. The solution introduces an unsupervised proxy task for learning user embeddings: user re-identification. Experiments with 16 different communities show that the resulting embeddings are more effective for community membership identification than common unsupervised representations.

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Sounding Board: A User-Centric and Content-Driven Social Chatbot
Hao Fang | Hao Cheng | Maarten Sap | Elizabeth Clark | Ari Holtzman | Yejin Choi | Noah A. Smith | Mari Ostendorf
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. We also share insights gained from large-scale online logs based on 160,000 conversations with real-world users.

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Conversation Modeling on Reddit Using a Graph-Structured LSTM
Victoria Zayats | Mari Ostendorf
Transactions of the Association for Computational Linguistics, Volume 6

This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and temporal conversation structure. In experiments with a task of predicting popularity of comments in Reddit discussions, the proposed model outperforms a node-independent architecture for different sets of input features. Analyses show a benefit to the model over the full course of the discussion, improving detection in both early and late stages. Further, the use of language cues with the bidirectional tree state updates helps with identifying controversial comments.

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Low-Rank RNN Adaptation for Context-Aware Language Modeling
Aaron Jaech | Mari Ostendorf
Transactions of the Association for Computational Linguistics, Volume 6

A context-aware language model uses location, user and/or domain metadata (context) to adapt its predictions. In neural language models, context information is typically represented as an embedding and it is given to the RNN as an additional input, which has been shown to be useful in many applications. We introduce a more powerful mechanism for using context to adapt an RNN by letting the context vector control a low-rank transformation of the recurrent layer weight matrix. Experiments show that allowing a greater fraction of the model parameters to be adjusted has benefits in terms of perplexity and classification for several different types of context.

2017

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A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
Hao Cheng | Hao Fang | Mari Ostendorf
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.

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Scientific Information Extraction with Semi-supervised Neural Tagging
Yi Luan | Mari Ostendorf | Hannaneh Hajishirzi
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.

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Language Based Mapping of Science Assessment Items to Skills
Farah Nadeem | Mari Ostendorf
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Knowledge of the association between assessment questions and the skills required to solve them is necessary for analysis of student learning. This association, often represented as a Q-matrix, is either hand-labeled by domain experts or learned as latent variables given a large student response data set. As a means of automating the match to formal standards, this paper uses neural text classification methods, leveraging the language in the standards documents to identify online text for a proxy training task. Experiments involve identifying the topic and crosscutting concepts of middle school science questions leveraging multi-task training. Results show that it is possible to automatically build a Q-matrix without student response data and using a modest number of hand-labeled questions.

2016

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Characterizing the Language of Online Communities and its Relation to Community Reception
Trang Tran | Mari Ostendorf
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Ji He | Mari Ostendorf | Xiaodong He | Jianshu Chen | Jianfeng Gao | Lihong Li | Li Deng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Phonological Pun-derstanding
Aaron Jaech | Rik Koncel-Kedziorski | Mari Ostendorf
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Neural Model for Language Identification in Code-Switched Tweets
Aaron Jaech | George Mulcaire | Mari Ostendorf | Noah A. Smith
Proceedings of the Second Workshop on Computational Approaches to Code Switching

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Learning Latent Local Conversation Modes for Predicting Comment Endorsement in Online Discussions
Hao Fang | Hao Cheng | Mari Ostendorf
Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media

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Hierarchical Character-Word Models for Language Identification
Aaron Jaech | George Mulcaire | Shobhit Hathi | Mari Ostendorf | Noah A. Smith
Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media

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Deep Reinforcement Learning with a Natural Language Action Space
Ji He | Jianshu Chen | Xiaodong He | Jianfeng Gao | Lihong Li | Li Deng | Mari Ostendorf
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Data Selection With Fewer Words
Amittai Axelrod | Philip Resnik | Xiaodong He | Mari Ostendorf
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Open-Domain Name Error Detection using a Multi-Task RNN
Hao Cheng | Hao Fang | Mari Ostendorf
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Talking to the crowd: What do people react to in online discussions?
Aaron Jaech | Victoria Zayats | Hao Fang | Mari Ostendorf | Hannaneh Hajishirzi
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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What Your Username Says About You
Aaron Jaech | Mari Ostendorf
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Aligning Sentences from Standard Wikipedia to Simple Wikipedia
William Hwang | Hannaneh Hajishirzi | Mari Ostendorf | Wei Wu
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Unediting: Detecting Disfluencies Without Careful Transcripts
Victoria Zayats | Mari Ostendorf | Hannaneh Hajishirzi
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013

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Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty
Julie Medero | Mari Ostendorf
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Detecting Forum Authority Claims in Online Discussions
Alex Marin | Bin Zhang | Mari Ostendorf
Proceedings of the Workshop on Language in Social Media (LSM 2011)

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Annotating Social Acts: Authority Claims and Alignment Moves in Wikipedia Talk Pages
Emily M. Bender | Jonathan T. Morgan | Meghan Oxley | Mark Zachry | Brian Hutchinson | Alex Marin | Bin Zhang | Mari Ostendorf
Proceedings of the Workshop on Language in Social Media (LSM 2011)

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Question Detection in Spoken Conversations Using Textual Conversations
Anna Margolis | Mari Ostendorf
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Automatic Generation of Personalized Annotation Tags for Twitter Users
Wei Wu | Bin Zhang | Mari Ostendorf
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification
Bin Zhang | Brian Hutchinson | Wei Wu | Mari Ostendorf
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Domain Adaptation with Unlabeled Data for Dialog Act Tagging
Anna Margolis | Karen Livescu | Mari Ostendorf
Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing

2009

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Mari Ostendorf | Michael Collins | Shri Narayanan | Douglas W. Oard | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Mari Ostendorf | Michael Collins | Shri Narayanan | Douglas W. Oard | Lucy Vanderwende
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Classifying Factored Genres with Part-of-Speech Histograms
Sergey Feldman | Marius Marin | Julie Medero | Mari Ostendorf
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Modeling Vocal Interaction for Text-Independent Participant Characterization in Multi-Party Conversation
Kornel Laskowski | Mari Ostendorf | Tanja Schultz
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

2007

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iROVER: Improving System Combination with Classification
Dustin Hillard | Bjoern Hoffmeister | Mari Ostendorf | Ralf Schlueter | Hermann Ney
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Modeling Vocal Interaction for Text-Independent Classification of Conversation Type
Kornel Laskowski | Mari Ostendorf | Tanja Schultz
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

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Problem-Sensitive Response Generation in Human-Robot Dialogs
Petra Gieselmann | Mari Ostendorf
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

2006

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Agreement/Disagreement Classification: Exploiting Unlabeled Data using Contrast Classifiers
Sangyun Hahn | Richard Ladner | Mari Ostendorf
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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SParseval: Evaluation Metrics for Parsing Speech
Brian Roark | Mary Harper | Eugene Charniak | Bonnie Dorr | Mark Johnson | Jeremy Kahn | Yang Liu | Mari Ostendorf | John Hale | Anna Krasnyanskaya | Matthew Lease | Izhak Shafran | Matthew Snover | Robin Stewart | Lisa Yung
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

While both spoken and written language processing stand to benefit from parsing, the standard Parseval metrics (Black et al., 1991) and their canonical implementation (Sekine and Collins, 1997) are only useful for text. The Parseval metrics are undefined when the words input to the parser do not match the words in the gold standard parse tree exactly, and word errors are unavoidable with automatic speech recognition (ASR) systems. To fill this gap, we have developed a publicly available tool for scoring parses that implements a variety of metrics which can handle mismatches in words and segmentations, including: alignment-based bracket evaluation, alignment-based dependency evaluation, and a dependency evaluation that does not require alignment. We describe the different metrics, how to use the tool, and the outcome of an extensive set of experiments on the sensitivity.

2005

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Effective Use of Prosody in Parsing Conversational Speech
Jeremy G. Kahn | Matthew Lease | Eugene Charniak | Mark Johnson | Mari Ostendorf
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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A Quantitative Analysis of Lexical Differences Between Genders in Telephone Conversations
Constantinos Boulis | Mari Ostendorf
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Reading Level Assessment Using Support Vector Machines and Statistical Language Models
Sarah Schwarm | Mari Ostendorf
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Detecting Structural Metadata with Decision Trees and Transformation-Based Learning
Joungbum Kim | Sarah E. Schwarm | Mari Ostendorf
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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Improving Automatic Sentence Boundary Detection with Confusion Networks
D. Hillard | M. Ostendorf | A. Stolcke | Y. Liu | E. Shriberg
Proceedings of HLT-NAACL 2004: Short Papers

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Parsing Conversational Speech Using Enhanced Segmentation
Jeremy G. Kahn | Mari Ostendorf | Ciprian Chelba
Proceedings of HLT-NAACL 2004: Short Papers

2003

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Getting More Mileage from Web Text Sources for Conversational Speech Language Modeling using Class-Dependent Mixtures
Ivan Bulyko | Mari Ostendorf | Andreas Stolcke
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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Detection Of Agreement vs. Disagreement In Meetings: Training With Unlabeled Data
Dustin Hillard | Mari Ostendorf | Elizabeth Shriberg
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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Directions For Multi-Party Human-Computer Interaction Research
Katrin Kirchhoff | Mari Ostendorf
Proceedings of the HLT-NAACL 2003 Workshop on Research Directions in Dialogue Processing

2001

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Improving Information Extraction by Modeling Errors in Speech Recognizer Output
David D. Palmer | Mari Ostendorf
Proceedings of the First International Conference on Human Language Technology Research

1994

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Language Modeling with Sentence-Level Mixtures
Rukmini Iyer | Mari Ostendorf | J. Robin Rohlicek
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Evaluating the Use of Prosodic Information in Speech Recognition and Understanding
M. Ostendorf | P. Price | S. Shattuck Hufnagel
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Segment-Based Acoustic Models for Continuous Speech Recognition
Mari Ostendorf | J. Robin Rohlicek
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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A Hierarchical Stochastic Model for Automatic Prediction of Prosodic Boundary Location
M Ostendorf | N Veilleux
Computational Linguistics, Volume 20, Number 1, March 1994

1993

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On the Use of Tied-Mixture Distributions
Owen Kimball | Mari Ostendorf
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Session 11: Prosody
M. Ostendorf
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Prosody/Parse Scoring and Its Application in ATIS
N. M. Veilleux | M. Ostendorf
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Evaluating the Use of Prosodic Information in Speech Recognition and Understanding
Mari Ostendorf | Patti Price
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Segment-Based Acoustic Models for Continuous Speech Recognition
Mari Ostendorf | J. Robin Rohlicek
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

1992

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Recognition Using Classification and Segmentation Scoring
Owen Kimball | Mari Ostendorf | Robin Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Probabilistic Parse Scoring Based on Prosodic Phrasing
N. M. Veilleux | M. Ostendorf
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Weight Estimation for N-Best Rescoring
Ashvin Kannan | Mari Ostendorf | J. Robin Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Evaluating the Use of Prosodic Information in Speech Recognition and Understanding
Mari Ostendorf | Patti Price
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Segment-Based Acoustic Models with Multi-level Search Algorithms for Continuous Speech Recognition
Mari Ostendorf | J. Robin Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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Integration of Diverse Recognition Methodologies Through Reevaluation of N-Best Sentence Hypotheses
M. Ostendorf | A. Kannan | S. Austin | O. Kimball | R. Schwartz | J.R. Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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Session 6: Demonstrations and Videotapes of Speech and Natural Language Technologies
Mari Ostendorf
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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A Dynamical System Approach to Continuous Speech Recognition
V. Digalakis | J.R. Rohlicek | M. Ostendorf
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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The Use of Prosody in Syntactic Disambiguation
Patti Price | Mari Ostendorf | Stefanie Shattuck-Hufnagel | Cynthia Fong
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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Use of Prosody in Syntactic Disambiguation: An Analysis-by-Synthesis Approach
C. W. Wightman | N. M. Veilleux | M. Ostendorf
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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Evaluating the Use of Prosodic Information in Speech Recognition and Understanding
Mari Ostendorf | Patti Price
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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Segment-Based Acoustic Models with Multi-level Search Algorithms for Continuous Speech Recognition
Mari Ostendorf | J. Robin Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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The Use of Relative Duration in Syntactic Disambiguation
M. Ostendorf | P. J. Price | J. Bear | C.W. Wightman
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Fast Search Algorithms for Connected Phone Recognition Using the Stochastic Segment Model
V. Digalakis | M. Ostendorf | J.R. Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Evaluating the Use of Prosodic Information in Speech Recognition and Understanding
Mari Ostendorf | Patti Price
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Segment-Based Acoustic Models with Multi-level Search Algorithms for Continuous Speech Recognition
Mari Ostendorf | J. Robin Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

1989

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Prosody and Parsing
P. J. Price | M. Ostendorf | C.W. Wightman
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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Improvements in the Stochastic Segment Model for Phoneme Recognition
V. Digalakis | M. Ostendorf | J.R. Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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Evaluating the Use of Prosodic Information in Speech Recognition and Understanding
Mari Ostendorf | Patti Price
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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Segment-Based Acoustic Models with Multi-level Search Algorithms for Continuous Speech Recognition
Mari Ostendorf | J. Robin Rohlicek
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989

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