David K. Evans

Also published as: David Evans, David Kirk Evans


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Pointwise Paraphrase Appraisal is Potentially Problematic
Hannah Chen | Yangfeng Ji | David Evans
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases. This pointwise-based evaluation method does not match well the objective of most real world applications, so the goal of our work is to understand how models which perform well under pointwise evaluation may fail in practice and find better methods for evaluating paraphrase identification models. As a first step towards that goal, we show that although the standard way of fine-tuning BERT for paraphrase identification by pairing two sentences as one sequence results in a model with state-of-the-art performance, that model may perform poorly on simple tasks like identifying pairs with two identical sentences. Moreover, we show that these models may even predict a pair of randomly-selected sentences with higher paraphrase score than a pair of identical ones.

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Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory
Hannah Chen | Yangfeng Ji | David Evans
Findings of the Association for Computational Linguistics: EMNLP 2020

Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models.


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A Japanese-English Technical Lexicon for Translation and Language Research
Fredric Gey | David Kirk Evans | Noriko Kando
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we present a Japanese-English Bilingual lexicon of technical terms. The lexicon was derived from the first and second NTCIR evaluation collections for research into cross-language information retrieval for Asian languages. While it can be utilized for translation between Japanese and English, the lexicon is also suitable for language research and language engineering. Since it is collection-derived, it contains instances of word variants and miss-spellings which make it eminently suitable for further research. For a subset of the lexicon we make available the collection statistics. In addition we make available a Katakana subset suitable for transliteration research.


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


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


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Evaluation of Computational Linguistic Techniques for Identifying Significant Topics for Browsing Applications
Judith L. Klavans | Nina Wacholder | David K. Evans
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Evaluation of Automatically Identified Index Terms for Browsing Electronic Documents
Nina Wacholder | Judith L. Klavans | David K. Evans
Sixth Applied Natural Language Processing Conference