David Reitter


2020

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Surprisal Predicts Code-Switching in Chinese-English Bilingual Text
Jesús Calvillo | Le Fang | Jeremy Cole | David Reitter
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Why do bilinguals switch languages within a sentence? The present observational study asks whether word surprisal and word entropy predict code-switching in bilingual written conversation. We describe and model a new dataset of Chinese-English text with 1476 clean code-switched sentences, translated back into Chinese. The model includes known control variables together with word surprisal and word entropy. We found that word surprisal, but not entropy, is a significant predictor that explains code-switching above and beyond other well-known predictors. We also found sentence length to be a significant predictor, which has been related to sentence complexity. We propose high cognitive effort as a reason for code-switching, as it leaves fewer resources for inhibition of the alternative language. We also corroborate previous findings, but this time using a computational model of surprisal, a new language pair, and doing so for written language.

2019

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Fusion of Detected Objects in Text for Visual Question Answering
Chris Alberti | Jeffrey Ling | Michael Collins | David Reitter
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The “Bounding Boxes in Text Transformer” (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark, achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided.

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Like a Baby: Visually Situated Neural Language Acquisition
Alexander Ororbia | Ankur Mali | Matthew Kelly | David Reitter
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.

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Treat the Word As a Whole or Look Inside? Subword Embeddings Model Language Change and Typology
Yang Xu | Jiasheng Zhang | David Reitter
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

We use a variant of word embedding model that incorporates subword information to characterize the degree of compositionality in lexical semantics. Our models reveal some interesting yet contrastive patterns of long-term change in multiple languages: Indo-European languages put more weight on subword units in newer words, while conversely Chinese puts less weights on the subwords, but more weight on the word as a whole. Our method provides novel evidence and methodology that enriches existing theories in evolutionary linguistics. The resulting word vectors also has decent performance in NLP-related tasks.

2018

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The Timing of Lexical Memory Retrievals in Language Production
Jeremy Cole | David Reitter
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper explores the time course of lexical memory retrieval by modeling fluent language production. The duration of retrievals is predicted using the ACT-R cognitive architecture. In a large-scale observational study of a spoken corpus, we find that language production at a time point preceding a word is sped up or slowed down depending on activation of that word. This computational analysis has consequences for the theoretical model of language production. The results point to interference between lexical and phonological stages as well as a quantifiable buffer for lexical information that opens up the possibility of non-sequential retrievals.

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Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power
Yang Xu | Jeremy Cole | David Reitter
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Linguistic alignment between dialogue partners has been claimed to be affected by their relative social power. A common finding has been that interlocutors of higher power tend to receive more alignment than those of lower power. However, these studies overlook some low-level linguistic features that can also affect alignment, which casts doubts on these findings. This work characterizes the effect of power on alignment with logistic regression models in two datasets, finding that the effect vanishes or is reversed after controlling for low-level features such as utterance length. Thus, linguistic alignment is explained better by low-level features than by social power. We argue that a wider range of factors, especially cognitive factors, need to be taken into account for future studies on observational data when social factors of language use are in question.

2017

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Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance
Yang Xu | David Reitter
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representations of the lexical entropy series of each interlocutor, power spectrum overlap (PSO) and relative phase (RP). We find that PSO is a negative predictor of task success, while RP is a positive one. An SVM with these features significantly improved on previous task success prediction models. Our findings suggest that the strategic distribution of information density between interlocutors is relevant to task success.

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Event Ordering with a Generalized Model for Sieve Prediction Ranking
Bill McDowell | Nathanael Chambers | Alexander Ororbia II | David Reitter
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper improves on several aspects of a sieve-based event ordering architecture, CAEVO (Chambers et al., 2014), which creates globally consistent temporal relations between events and time expressions. First, we examine the usage of word embeddings and semantic role features. With the incorporation of these new features, we demonstrate a 5% relative F1 gain over our replicated version of CAEVO. Second, we reformulate the architecture’s sieve-based inference algorithm as a prediction reranking method that approximately optimizes a scoring function computed using classifier precisions. Within this prediction reranking framework, we propose an alternative scoring function, showing an 8.8% relative gain over the original CAEVO. We further include an in-depth analysis of one of the main datasets that is used to evaluate temporal classifiers, and we show how despite using the densest corpus, there is still a danger of overfitting. While this paper focuses on temporal ordering, its results are applicable to other areas that use sieve-based architectures.

2016

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Entropy Converges Between Dialogue Participants: Explanations from an Information-Theoretic Perspective
Yang Xu | David Reitter
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Convergence of Syntactic Complexity in Conversation
Yang Xu | David Reitter
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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Learning a Deep Hybrid Model for Semi-Supervised Text Classification
Alexander Ororbia II | C. Lee Giles | David Reitter
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Pragmatic Alignment on Social Support Type in Health Forum Conversations
Yafei Wang | John Yen | David Reitter
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

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An Evaluation and Comparison of Linguistic Alignment Measures
Yang Xu | David Reitter
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

2014

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A Model to Qualify the Linguistic Adaptation Phenomenon in Online Conversation Threads: Analyzing Priming Effect in Online Health Community
Yafei Wang | David Reitter | John Yen
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics

2012

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Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)
David Reitter | Roger Levy
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)

2011

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Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
Frank Keller | David Reitter
Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics

2010

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Did Social Networks Shape Language Evolution? A Multi-Agent Cognitive Simulation
David Reitter | Christian Lebiere
Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics

2007

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Predicting Success in Dialogue
David Reitter | Johanna D. Moore
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Dimensionality Reduction Aids Term Co-Occurrence Based Multi-Document Summarization
Ben Hachey | Gabriel Murray | David Reitter
Proceedings of the Workshop on Task-Focused Summarization and Question Answering

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Priming Effects in Combinatory Categorial Grammar
David Reitter | Julia Hockenmaier | Frank Keller
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Computational Modelling of Structural Priming in Dialogue
David Reitter | Frank Keller | Johanna D. Moore
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

2004

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UI on the Fly: Generating a Multimodal User Interface
David Reitter | Erin Panttaja | Fred Cummins
Proceedings of HLT-NAACL 2004: Short Papers

2003

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Step by step: underspecified markup in incremental rhetorical analysis
David Reitter | Manfred Stede
Proceedings of 4th International Workshop on Linguistically Interpreted Corpora (LINC-03) at EACL 2003

2002

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XML/XSL in the Dictionary: The Case of Discourse Markers
Daniela Berger | David Reitter | Manfred Stede
COLING-02: The 2nd Workshop on NLP and XML (NLPXML-2002)