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Papers - Search Results

Query: keyword "2019"

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[Alhindi et al., 2019]

Tariq Alhindi, Jonas Pfeiffer, and Smaranda Muresan. Fine-tuned neural models for propaganda detection at the sentence and fragment levels. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 98-102, Hong Kong, China, November 2019. Association for Computational Linguistics.

[Biru et al., 2019]

Elshadai Tesfaye Biru, Yishak Tofik Mohammed, David Tofu, Erica Cooper, and Julia Hirschberg. Subset selection, adaptation, gemination and prosody prediction for amharic text-to-speech synthesis. In 10th ISCA Speech Synthesis Workshop, 2019.

[Chakrabarty et al., 2019a]

Tuhin Chakrabarty, Kilol Gupta, and Smaranda Muresan. Pay ``attention'' to your context when classifying abusive language. In Proceedings of the Third Workshop on Abusive Language Online, pages 70-79, Florence, Italy, August 2019. Association for Computational Linguistics.

[Chakrabarty et al., 2019b]

Tuhin Chakrabarty, Christopher Hidey, and Kathy McKeown. IMHO fine-tuning improves claim detection. In 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), pages 558-563, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.

[Chakrabarty et al., 2019c]

Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan, Kathy McKeown, and Alyssa Hwang. AMPERSAND: Argument mining for PERSuAsive oNline discussions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2933-2943, Hong Kong, China, November 2019. Association for Computational Linguistics.

[Chang and McKeown, 2019]

Serina Chang and Kathy McKeown. Automatically inferring gender associations from language. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5746-5752, Hong Kong, China, November 2019. Association for Computational Linguistics.

[Eskander et al., 2019]

Ramy Eskander, Judith Klavans, and Smaranda Muresan. Unsupervised morphological segmentation for low-resource polysynthetic languages. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 189-195, Florence, Italy, August 2019. Association for Computational Linguistics.

[Gao et al., 2019]

Yanjun Gao, Alex Driban, Brennan Xavier McManus, Elena Musi, Patricia Davies, Smaranda Muresan, and Rebecca J. Passonneau. Rubric reliability and annotation of content and argument in source-based argument essays. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 507-518, Florence, Italy, August 2019. Association for Computational Linguistics.

[Gero et al., 2019]

Katy Gero, Chris Kedzie, Jonathan Reeve, and Lydia Chilton. Low level linguistic controls for style transfer and content preservation. In Proceedings of the 12th International Conference on Natural Language Generation, pages 208-218, Tokyo, Japan, October--November 2019. Association for Computational Linguistics.

[Hidey and McKeown, 2019]

Christopher Hidey and Kathy McKeown. Fixed that for you: Generating contrastive claims with semantic edits. In 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), pages 1756-1767, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.

[Kedzie and McKeown, 2019]

Chris Kedzie and Kathleen McKeown. A good sample is hard to find: Noise injection sampling and self-training for neural language generation models. In Proceedings of the 12th International Conference on Natural Language Generation, pages 584-593, Tokyo, Japan, October--November 2019. Association for Computational Linguistics.

[Lee et al., 2019]

Fei-Tzin Lee, Derrick Hull, Jacob Levine, Bonnie Ray, and Kathy McKeown. Identifying therapist conversational actions across diverse psychotherapeutic approaches. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 12-23, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.

[Ouyang and McKeown, 2019]

Jessica Ouyang and Kathy McKeown. Neural network alignment for sentential paraphrases. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4724-4735, Florence, Italy, July 2019. Association for Computational Linguistics.

[Ouyang et al., 2019]

Jessica Ouyang, Boya Song, and Kathy McKeown. A robust abstractive system for cross-lingual summarization. In 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), pages 2025-2031, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.

[Rasooli and Collins, 2019]

Mohammad Sadegh Rasooli and Michael Collins. Low-resource syntactic transfer with unsupervised source reordering. In 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), pages 3845-3856, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.

[Sloan et al., 2019]

Rose Sloan, Syed Sarfaraz Akhtar, Bryan Li, Ritvik Shrivastava, Agust́ın Gravano, and Julia Hirschberg. Prosody prediction from syntactic, lexical, and word embedding features. In 10th ISCA Speech Synthesis Workshop, 2019.

[Soto and Hirschberg, 2019]

Victor Soto and Julia Hirschberg. Improving code-switched language modeling performance using cognate features. In INTERSPEECH 2019, 2019.

[Turcan and McKeown, 2019]

Elsbeth Turcan and Kathy McKeown. Dreaddit: A Reddit dataset for stress analysis in social media. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 97-107, Hong Kong, November 2019. Association for Computational Linguistics.

[Ulinski and Hirschberg, 2019]

Morgan Ulinski and Julia Hirschberg. Crowdsourced hedge term disambiguation. In Proceedings of the 13th Linguistic Annotation Workshop, pages 1-5, Florence, Italy, August 2019. Association for Computational Linguistics.

[Ulinski et al., 2019]

Morgan Ulinski, Bob Coyne, and Julia Hirschberg. SpatialNet: A declarative resource for spatial relations. In Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP), pages 61-70, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.

[Varia et al., 2019]

Siddharth Varia, Christopher Hidey, and Tuhin Chakrabarty. Discourse relation prediction: Revisiting word pairs with convolutional networks. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 442-452, Stockholm, Sweden, September 2019. Association for Computational Linguistics.

[Weise et al., 2019]

Anna Weise, Sarah Ita Levitan, Julia Hirschberg, and Rivka Levitan. Individual differences in acoustic-prosodic entrainment in spoken dialogue. Speech Communication, 115:78-87, 2019.

[Yang and Hirschberg, 2019]

Zixiaofan Yang and Julia Hirschberg. Linguistically-informed training of acoustic word embeddings for low-resource languages. In INTERSPEECH 2019, 2019.

[Yang et al., 2019a]

Zixiaofan Yang, Lin Ai, and Julia Hirschberg. Multimodal indicators of humor in videos. 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pages 538-543, 2019.

[Yang et al., 2019b]

Zixiaofan Yang, Bingyan Hu, and Julia Hirschberg. Predicting humor by learning from time-aligned comments. In INTERSPEECH 2019, 2019.

[Zhong et al., 2019]

Ruiqi Zhong, Yanda Chen, Desmond Patton, Charlotte Selous, and Kathy McKeown. Detecting and reducing bias in a high stakes domain. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4765-4775, Hong Kong, China, November 2019. Association for Computational Linguistics.

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