Vera Demberg


2021

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Time-Aware Ancient Chinese Text Translation and Inference
Ernie Chang | Yow-Ting Shiue | Hui-Syuan Yeh | Vera Demberg
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text. To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era. We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information. We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs. We release both the code and the data for future research.

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Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning
Ernie Chang | Hui-Syuan Yeh | Vera Demberg
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Recent advancements in data-to-text generation largely take on the form of neural end-to-end systems. Efforts have been dedicated to improving text generation systems by changing the order of training samples in a process known as curriculum learning. Past research on sequence-to-sequence learning showed that curriculum learning helps to improve both the performance and convergence speed. In this work, we delve into the same idea surrounding the training samples consisting of structured data and text pairs, where at each update, the curriculum framework selects training samples based on the model’s competence. Specifically, we experiment with various difficulty metrics and put forward a soft edit distance metric for ranking training samples. On our benchmarks, it shows faster convergence speed where training time is reduced by 38.7% and performance is boosted by 4.84 BLEU.

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Neural Data-to-Text Generation with LM-based Text Augmentation
Ernie Chang | Xiaoyu Shen | Dawei Zhu | Vera Demberg | Hui Su
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instances are available on the data side, often only very few text samples are available. To address this problem, we here propose a novel few-shot approach for this setting. Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples. As the text augmentation can introduce noise to the training data, we use cycle consistency as an objective, in order to make sure that a given data sample can be correctly reconstructed after having been formulated as text (and that text samples can be reconstructed from data). On both the E2E and WebNLG benchmarks, we show that this weakly supervised training paradigm is able to outperform fully supervised sequence-to-sequence models with less than 10% of the training set. By utilizing all annotated data, our model can boost the performance of a standard sequence-to-sequence model by over 5 BLEU points, establishing a new state-of-the-art on both datasets.

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Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
Ernie Chang | Vera Demberg | Alex Marin
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expert-curated and the rest of the data is automatically annotated. We follow that approach, by automatically constructing a large-scale weakly-labeled data with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly train the NLG and NLU models. The proposed framework adapts the parameter updates to the models according to the estimated label-quality. On both the E2E and Weather benchmarks, we show that this weakly supervised training paradigm is an effective approach under low resource scenarios with as little as 10 data instances, and outperforming benchmark systems on both datasets when 100% of the training data is used.

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On Training Instance Selection for Few-Shot Neural Text Generation
Ernie Chang | Xiaoyu Shen | Hui-Syuan Yeh | Vera Demberg
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. The code and training data are made available. We hope that this work will call for more attention on this largely unexplored area.

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Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain
Wei Shi | Vera Demberg
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.

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The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Ernie Chang | Xiaoyu Shen | Alex Marin | Vera Demberg
Proceedings of the 14th International Conference on Natural Language Generation

We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. Studying the selection strategy can help us (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.

2020

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Story Generation with Rich Details
Fangzhou Zhai | Vera Demberg | Alexander Koller
Proceedings of the 28th International Conference on Computational Linguistics

Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.

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DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
Ernie Chang | Jeriah Caplinger | Alex Marin | Xiaoyu Shen | Vera Demberg
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree structure. By using a backend sequence-to-sequence model, our system iteratively analyzes the annotated labels in order to better sample unlabeled data. In a simulation experiment performed on annotating large quantities of structured data, DART has been shown to reduce the total number of annotations needed with active learning and automatically suggesting relevant labels.

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Diverse and Relevant Visual Storytelling with Scene Graph Embeddings
Xudong Hong | Rakshith Shetty | Asad Sayeed | Khushboo Mehra | Vera Demberg | Bernt Schiele
Proceedings of the 24th Conference on Computational Natural Language Learning

A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics.

2019

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Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains
Wei Shi | Vera Demberg
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to solve the task, it is however additionally necessary to capture what events are expected to cause or follow each other. Current discourse relation classifiers fall short in this respect. We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a next-sentence prediction task, and thus encode a representation of likely next sentences. The BERT-based model outperforms the current state of the art in 11-way classification by 8% points on the standard PDTB dataset. Our experiments also demonstrate that the model can be successfully ported to other domains: on the BioDRB dataset, the model outperforms the state of the art system around 15% points.

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Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders
Xudong Hong | Ernie Chang | Vera Demberg
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the third rank without using data augmentation techniques or additional components (such as a re-ranker).

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Proceedings of the 13th International Conference on Computational Semantics - Long Papers
Simon Dobnik | Stergios Chatzikyriakidis | Vera Demberg
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

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Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification
Wei Shi | Vera Demberg
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial importance. We here propose a new model for implicit discourse relation classification, which consists of a classifier, and a sequence-to-sequence model which is trained to generate a representation of the discourse relational arguments by trying to predict the relational arguments including a suitable implicit connective. Training is possible because such implicit connectives have been annotated as part of the PDTB corpus. Along with a memory network, our model could generate more refined representations for the task. And on the now standard 11-way classification, our method outperforms the previous state of the art systems on the PDTB benchmark on multiple settings including cross validation.

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Proceedings of the 13th International Conference on Computational Semantics - Short Papers
Simon Dobnik | Stergios Chatzikyriakidis | Vera Demberg
Proceedings of the 13th International Conference on Computational Semantics - Short Papers

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Proceedings of the 13th International Conference on Computational Semantics - Student Papers
Simon Dobnik | Stergios Chatzikyriakidis | Vera Demberg | Kathrein Abu Kwaik | Vladislav Maraev
Proceedings of the 13th International Conference on Computational Semantics - Student Papers

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Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
Wei Shi | Frances Yung | Vera Demberg
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connectives as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca. 18k instances in the Penn Discourse Treebank (PDTB)). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.

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Verb-Second Effect on Quantifier Scope Interpretation
Asad Sayeed | Matthias Lindemann | Vera Demberg
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Sentences like “Every child climbed a tree” have at least two interpretations depending on the precedence order of the universal quantifier and the indefinite. Previous experimental work explores the role that different mechanisms such as semantic reanalysis and world knowledge may have in enabling each interpretation. This paper discusses a web-based task that uses the verb-second characteristic of German main clauses to estimate the influence of word order variation over world knowledge.

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A Hybrid Model for Globally Coherent Story Generation
Fangzhou Zhai | Vera Demberg | Pavel Shkadzko | Wei Shi | Asad Sayeed
Proceedings of the Second Workshop on Storytelling

Automatically generating globally coherent stories is a challenging problem. Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences. Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite data-demanding. We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus. The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan. Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.

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Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task
Frances Yung | Vera Demberg | Merel Scholman
Proceedings of the 13th Linguistic Annotation Workshop

The perspective of being able to crowd-source coherence relations bears the promise of acquiring annotations for new texts quickly, which could then increase the size and variety of discourse-annotated corpora. It would also open the avenue to answering new research questions: Collecting annotations from a larger number of individuals per instance would allow to investigate the distribution of inferred relations, and to study individual differences in coherence relation interpretation. However, annotating coherence relations with untrained workers is not trivial. We here propose a novel two-step annotation procedure, which extends an earlier method by Scholman and Demberg (2017a). In our approach, coherence relation labels are inferred from connectives that workers insert into the text. We show that the proposed method leads to replicable coherence annotations, and analyse the agreement between the obtained relation labels and annotations from PDTB and RSTDT on the same texts.

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Proceedings of the 1st Workshop on Discourse Structure in Neural NLG
Anusha Balakrishnan | Vera Demberg | Chandra Khatri | Abhinav Rastogi | Donia Scott | Marilyn Walker | Michael White
Proceedings of the 1st Workshop on Discourse Structure in Neural NLG

2018

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Rollenwechsel-English: a large-scale semantic role corpus
Asad Sayeed | Pavel Shkadzko | Vera Demberg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A vision-grounded dataset for predicting typical locations for verbs
Nelson Mukuze | Anna Rohrbach | Vera Demberg | Bernt Schiele
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Do Speakers Produce Discourse Connectives Rationally?
Frances Yung | Vera Demberg
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice. One recent account is the Rational Speech Acts theory, which predicts that speakers try to maximize the informativeness of an utterance such that the listener can interpret the intended meaning correctly. Existing prior work uses referential language games to test the rational account of speakers’ production of concrete meanings, such as identification of objects within a picture. Building on the same paradigm, we design a novel Discourse Continuation Game to investigate speakers’ production of abstract discourse relations. Experimental results reveal that speakers significantly prefer a more informative connective, in line with predictions of the RSA model.

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Using Universal Dependencies in cross-linguistic complexity research
Aleksandrs Berdicevskis | Çağrı Çöltekin | Katharina Ehret | Kilu von Prince | Daniel Ross | Bill Thompson | Chunxiao Yan | Vera Demberg | Gary Lupyan | Taraka Rama | Christian Bentz
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks. We propose a method of estimating robustness of the complexity values obtained using a given measure and a given treebank. The results indicate that measures of syntactic complexity might be on average less robust than those of morphological complexity. We also estimate the validity of complexity measures by comparing the results for very similar languages and checking for unexpected differences. We show that some of those differences that arise can be diminished by using parallel treebanks and, more importantly from the practical point of view, by harmonizing the language-specific solutions in the UD annotation.

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Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning
David M. Howcroft | Dietrich Klakow | Vera Demberg
Proceedings of the 11th International Conference on Natural Language Generation

Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets.

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Learning distributed event representations with a multi-task approach
Xudong Hong | Asad Sayeed | Vera Demberg
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.

2017

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Crowdsourcing discourse interpretations: On the influence of context and the reliability of a connective insertion task
Merel Scholman | Vera Demberg
Proceedings of the 11th Linguistic Annotation Workshop

Traditional discourse annotation tasks are considered costly and time-consuming, and the reliability and validity of these tasks is in question. In this paper, we investigate whether crowdsourcing can be used to obtain reliable discourse relation annotations. We also examine the influence of context on the reliability of the data. The results of a crowdsourced connective insertion task showed that the method can be used to obtain reliable annotations: The majority of the inserted connectives converged with the original label. Further, the method is sensitive to the fact that multiple senses can often be inferred for a single relation. Regarding the presence of context, the results show no significant difference in distributions of insertions between conditions overall. However, a by-item comparison revealed several characteristics of segments that determine whether the presence of context makes a difference in annotations. The findings discussed in this paper can be taken as evidence that crowdsourcing can be used as a valuable method to obtain insights into the sense(s) of relations.

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G-TUNA: a corpus of referring expressions in German, including duration information
David Howcroft | Jorrig Vogels | Vera Demberg
Proceedings of the 10th International Conference on Natural Language Generation

Corpora of referring expressions elicited from human participants in a controlled environment are an important resource for research on automatic referring expression generation. We here present G-TUNA, a new corpus of referring expressions for German. Using the furniture stimuli set developed for the TUNA and D-TUNA corpora, our corpus extends on these corpora by providing data collected in a simulated driving dual-task setting, and additionally provides exact duration annotations for the spoken referring expressions. This corpus will hence allow researchers to analyze the interaction between referring expression length and speech rate, under conditions where the listener is under high vs. low cognitive load.

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Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
Wei Shi | Frances Yung | Raphael Rubino | Vera Demberg
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional training data from parallel corpora: When humans translate a text, they sometimes add connectives (a process known as explicitation). We automatically back-translate it into an English connective and use it to infer a label with high confidence. We show that a training set several times larger than the original training set can be generated this way. With the extra labeled instances, we show that even a simple bidirectional Long Short-Term Memory Network can outperform the current state-of-the-art.

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Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Ashutosh Modi | Ivan Titov | Vera Demberg | Asad Sayeed | Manfred Pinkal
Transactions of the Association for Computational Linguistics, Volume 5

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.

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A Systematic Study of Neural Discourse Models for Implicit Discourse Relation
Attapol Rutherford | Vera Demberg | Nianwen Xue
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Many neural network models have been proposed to tackle this problem. However, the comparison for this task is not unified, so we could hardly draw clear conclusions about the effectiveness of various architectures. Here, we propose neural network models that are based on feedforward and long-short term memory architecture and systematically study the effects of varying structures. To our surprise, the best-configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Further, we compare our best feedforward system with competitive convolutional and recurrent networks and find that feedforward can actually be more effective. For the first time for this task, we compile and publish outputs from previous neural and non-neural systems to establish the standard for further comparison.

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Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking
David M. Howcroft | Vera Demberg
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

While previous research on readability has typically focused on document-level measures, recent work in areas such as natural language generation has pointed out the need of sentence-level readability measures. Much of psycholinguistics has focused for many years on processing measures that provide difficulty estimates on a word-by-word basis. However, these psycholinguistic measures have not yet been tested on sentence readability ranking tasks. In this paper, we use four psycholinguistic measures: idea density, surprisal, integration cost, and embedding depth to test whether these features are predictive of readability levels. We find that psycholinguistic features significantly improve performance by up to 3 percentage points over a standard document-level readability metric baseline.

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On the Need of Cross Validation for Discourse Relation Classification
Wei Shi | Vera Demberg
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21 of the PDTB and test on section 23, which includes a total of 761 implicit discourse relations. In this paper, we’d like to make a methodological point, arguing that the standard test set is too small to draw conclusions about whether the inclusion of certain features constitute a genuine improvement, or whether one got lucky with some properties of the test set, and argue for the adoption of cross validation for the discourse relation classification task by the community.

2016

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Event participant modelling with neural networks
Ottokar Tilk | Vera Demberg | Asad Sayeed | Dietrich Klakow | Stefan Thater
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Roleo: Visualising Thematic Fit Spaces on the Web
Asad Sayeed | Xudong Hong | Vera Demberg
Proceedings of ACL-2016 System Demonstrations

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Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks
Ines Rehbein | Merel Scholman | Vera Demberg
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.

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Thematic fit evaluation: an aspect of selectional preferences
Asad Sayeed | Clayton Greenberg | Vera Demberg
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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How can we adapt generation to the user’s cognitive load?
Vera Demberg
Proceedings of the 9th International Natural Language Generation conference

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Improving event prediction by representing script participants
Simon Ahrendt | Vera Demberg
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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LingoTurk: managing crowdsourced tasks for psycholinguistics
Florian Pusse | Asad Sayeed | Vera Demberg
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation
Maximilian Schwenger | Álvaro Torralba | Joerg Hoffmann | David M. Howcroft | Vera Demberg
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The search space in grammar-based natural language generation tasks can get very large, which is particularly problematic when generating long utterances or paragraphs. Using surface realization with OpenCCG as an example, we show that we can effectively detect partial solutions (edges) which cannot ultimately be part of a complete sentence because of their syntactic category. Formulating the completion of an edge into a sentence as finding a solution path in a large state-transition system, we demonstrate a connection to AI Planning which is concerned with this kind of problem. We design a compilation from OpenCCG into AI Planning allowing the detection of infeasible edges via AI Planning dead-end detection methods (proving the absence of a solution to the compilation). Our experiments show that this can filter out large fractions of infeasible edges in, and thus benefit the performance of, complex realization processes.

2015

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Uniform Surprisal at the Level of Discourse Relations: Negation Markers and Discourse Connective Omission
Fatemeh Torabi Asr | Vera Demberg
Proceedings of the 11th International Conference on Computational Semantics

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Verb polysemy and frequency effects in thematic fit modeling
Clayton Greenberg | Vera Demberg | Asad Sayeed
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

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Towards Flexible, Small-Domain Surface Generation: Combining Data-Driven and Grammatical Approaches
Andrea Fischer | Vera Demberg | Dietrich Klakow
Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)

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Learning to predict script events from domain-specific text
Rachel Rudinger | Vera Demberg | Ashutosh Modi | Benjamin Van Durme | Manfred Pinkal
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Vector-space calculation of semantic surprisal for predicting word pronunciation duration
Asad Sayeed | Stefan Fischer | Vera Demberg
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)

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Improving unsupervised vector-space thematic fit evaluation via role-filler prototype clustering
Clayton Greenberg | Asad Sayeed | Vera Demberg
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics
Vera Demberg | Timothy O’Donnell
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics

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Incremental Semantic Role Labeling with Tree Adjoining Grammar
Ioannis Konstas | Frank Keller | Vera Demberg | Mirella Lapata
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)
Vera Demberg | Roger Levy
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

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The semantic augmentation of a psycholinguistically-motivated syntactic formalism
Asad Sayeed | Vera Demberg
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

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On the Information Conveyed by Discourse Markers
Fatemeh Torabi Asr | Vera Demberg
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

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Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar
Vera Demberg | Frank Keller | Alexander Koller
Computational Linguistics, Volume 39, Issue 4 - December 2013

2012

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Syntactic Surprisal Affects Spoken Word Duration in Conversational Contexts
Vera Demberg | Asad Sayeed | Philip Gorinski | Nikolaos Engonopoulos
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Incremental Neo-Davidsonian semantic construction for TAG
Asad Sayeed | Vera Demberg
Proceedings of the 11th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+11)

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Incremental Derivations in CCG
Vera Demberg
Proceedings of the 11th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+11)

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Measuring the Strength of Linguistic Cues for Discourse Relations
Fatemeh Torabi Asr | Vera Demberg
Proceedings of the Workshop on Advances in Discourse Analysis and its Computational Aspects

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German and English Treebanks and Lexica for Tree-Adjoining Grammars
Miriam Kaeshammer | Vera Demberg
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present a treebank and lexicon for German and English, which have been developed for PLTAG parsing. PLTAG is a psycholinguistically motivated, incremental version of tree-adjoining grammar (TAG). The resources are however also applicable to parsing with other variants of TAG. The German PLTAG resources are based on the TIGER corpus and, to the best of our knowledge, constitute the first scalable German TAG grammar. The English PLTAG resources go beyond existing resources in that they include the NP annotation by (Vadas and Curran, 2007), and include the prediction lexicon necessary for PLTAG.

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Implicitness of Discourse Relations
Fatemeh Torabi Asr | Vera Demberg
Proceedings of COLING 2012

2011

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A Strategy for Information Presentation in Spoken Dialog Systems
Vera Demberg | Andi Winterboer | Johanna D. Moore
Computational Linguistics, Volume 37, Issue 3 - September 2011

2010

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Syntactic and Semantic Factors in Processing Difficulty: An Integrated Measure
Jeff Mitchell | Mirella Lapata | Vera Demberg | Frank Keller
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Proceedings of the Student Research Workshop at EACL 2009
Vera Demberg | Yanjun Ma | Nils Reiter
Proceedings of the Student Research Workshop at EACL 2009

2008

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A Psycholinguistically Motivated Version of TAG
Vera Demberg | Frank Keller
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

2007

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A Language-Independent Unsupervised Model for Morphological Segmentation
Vera Demberg
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Phonological Constraints and Morphological Preprocessing for Grapheme-to-Phoneme Conversion
Vera Demberg | Helmut Schmid | Gregor Möhler
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Information Presentation in Spoken Dialogue Systems
Vera Demberg | Johanna D. Moore
11th Conference of the European Chapter of the Association for Computational Linguistics