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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 25 1 Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? Danish Pruthi; Rachit Bansal; Bhuwan Dhingra... In: Transactions of the Association for Computational Linguistics, Vol 10, Pp 359-375 (2022) (2022) BASE Show details 2 WikiAsp: A Dataset for Multi-domain Aspect-based Summarization Hiroaki Hayashi; Prashant Budania; Peng Wang... In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 211-225 (2021) (2021) BASE Show details 3 Lexically Aware Semi-Supervised Learning for OCR Post-Correction Shruti Rijhwani; Daisy Rosenblum; Antonios Anastasopoulos... In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1285-1302 (2021) (2021) BASE Show details 4 How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering Zhengbao Jiang; Jun Araki; Haibo Ding; Graham Neubig In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 962-977 (2021) (2021) Abstract: AbstractRecent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to provide appropriate answers in many cases. In this paper, we ask the question, “How can we know when language models know, with confidence, the answer to a particular query?” We examine this question from the point of view of calibration, the property of a probabilistic model’s predicted probabilities actually being well correlated with the probabilities of correctness. We examine three strong generative models—T5, BART, and GPT-2—and study whether their probabilities on QA tasks are well calibrated, finding the answer is a relatively emphatic no. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness through fine-tuning, post-hoc probability modification, or adjustment of the predicted outputs or inputs. Experiments on a diverse range of datasets demonstrate the effectiveness of our methods. We also perform analysis to study the strengths and limitations of these methods, shedding light on further improvements that may be made in methods for calibrating LMs. We have released the code at https://github.com/jzbjyb/lm-calibration. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/2dc285d060674cb7be1481e110573a1f https://doi.org/10.1162/tacl_a_00407 BASE Hide details 5 Reducing Confusion in Active Learning for Part-Of-Speech Tagging Aditi Chaudhary; Antonios Anastasopoulos; Zaid Sheikh... In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1-16 (2021) (2021) BASE Show details 6 MasakhaNER: Named Entity Recognition for African Languages David Ifeoluwa Adelani; Jade Abbott; Graham Neubig... In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1116-1131 (2021) (2021) BASE Show details 7 Collection of a simultaneous translation corpus for comparative analysis Hiroaki Shimizu; Graham Neubig; Sakriani Sakti... In: http://www.lrec-conf.org/proceedings/lrec2014/pdf/162_Paper.pdf (2014) BASE Show details 8 A monotonic statistical machine translation approach to speaking style transformation Graham Neubig; Yuya Akita; Shinsuke Mori... In: http://www.phontron.com/paper/neubig12csl.pdf (2012) BASE Show details 9 An Event-Related Brain Potential Study on the Impact of Speech Recognition Errors Sakriani Sakti; Yu Odagaki; Takafumi Sasakura... In: http://www.phontron.com/paper/sakti14apsipa.pdf BASE Show details 10 The NAIST English Speech Recognition System for IWSLT 2015 Quoc Michael Heck; Truong Do; Sakriani Sakti... In: http://www.phontron.com/paper/heck15iwslt.pdf BASE Show details 11 The NAIST English Speech Recognition System for IWSLT 2015 Quoc Michael Heck; Truong Do; Sakriani Sakti... In: http://workshop2015.iwslt.org/downloads/IWSLT_2015_EP_8.pdf BASE Show details 12 Rule-based Syntactic Preprocessing for Syntax-based Machine Translation Yuto Hatakoshi; Graham Neubig; Sakriani Sakti... In: http://www.aclweb.org/anthology/W/W14/W14-4004.pdf BASE Show details 13 Unsupervised Learning of Lexical Information for Language Processing Systems Graham Neubig In: http://www.phontron.com/paper/neubig-thesis.pdf BASE Show details 14 Data-Driven Generation of Text Balloons based on Linguistic and Acoustic Features of a Comics-Anime Corpus Sho Matsumiya; Sakriani Sakti; Graham Neubig... In: http://www.phontron.com/paper/matsumiya14interspeech.pdf BASE Show details 15 INTERSPEECH 2010 Learning a Language Model from Continuous Speech Graham Neubig; Masato Mimura; Shinsuke Mori... In: http://www.ar.media.kyoto-u.ac.jp/EN/bib/intl/NEU-INTERSP10.pdf BASE Show details 16 Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015 Graham Neubig; Makoto Morishita; Satoshi Nakamura In: http://www.phontron.com/paper/neubig15wat.pdf BASE Show details 17 Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015 Graham Neubig; Makoto Morishita; Satoshi Nakamura In: http://wing.comp.nus.edu.sg/%7Eantho/W/W15/W15-5003.pdf BASE Show details 18 Substring-based Machine Translation Graham Neubig; Taro Watanabe; Shinsuke Mori... In: http://www.phontron.com/paper/neubig13mtj.pdf BASE Show details 19 TOWARDS LANGUAGE PRESERVATION: PRELIMINARY COLLECTION AND VOWEL ANALYSIS OF INDONESIAN ETHNIC SPEECH DATA Auliya Sani; Sakriani Sakti; Graham Neubig... In: http://www.phontron.com/paper/sani12ococosda.pdf BASE Show details 20 TOWARDS LANGUAGE PRESERVATION: PRELIMINARY COLLECTION AND VOWEL ANALYSIS OF INDONESIAN ETHNIC SPEECH DATA Auliya Sani; Sakriani Sakti; Graham Neubig... In: http://spalab.naist.jp/~tomoki/Tomoki/Conferences/OCOCOSDA2012_LangPreserv.pdf BASE Show details Page: 1 2

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