Search Results - "Neubig, Graham"

How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering by Jiang, Zhengbao, Araki, Jun, Ding, Haibo, Neubig, Graham

“…Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect,…”
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How Can We Know What Language Models Know? by Jiang, Zhengbao, Xu, Frank F., Araki, Jun, Neubig, Graham

“…Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as…”
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Can We Automate Scientific Reviewing? by Yuan, Weizhe, Liu, Pengfei, Neubig, Graham

“…The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications. At the same time, the…”
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Learning to mine aligned code and natural language pairs from stack overflow by Yin, Pengcheng, Deng, Bowen, Chen, Edgar, Vasilescu, Bogdan, Neubig, Graham

“…For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating…”
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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation by Sperber, Matthias, Neubig, Graham, Niehues, Jan, Waibel, Alex

“…Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a…”
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Optimizing segmentation granularity for neural machine translation by Salesky, Elizabeth, Runge, Andrew, Coda, Alex, Niehues, Jan, Neubig, Graham

“…In neural machine translation (NMT), it has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent…”
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The Return of Lexical Dependencies: Neural Lexicalized PCFGs by Zhu, Hao, Bisk, Yonatan, Neubig, Graham

“…In this paper we demonstrate that . This contrasts to the most popular current methods for grammar induction, which focus on discovering constituents…”
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Lexically Aware Semi-Supervised Learning for OCR Post-Correction by Rijhwani, Shruti, Rosenblum, Daisy, Anastasopoulos, Antonios, Neubig, Graham

“…Much of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents. Optical character recognition (OCR)…”
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WikiAsp: A Dataset for Multi-domain Aspect-based Summarization by Hayashi, Hiroaki, Budania, Prashant, Wang, Peng, Ackerson, Chris, Neervannan, Raj, Neubig, Graham

“…Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text,…”
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Reducing Confusion in Active Learning for Part-Of-Speech Tagging by Chaudhary, Aditi, Anastasopoulos, Antonios, Sheikh, Zaid, Neubig, Graham

“…Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building…”
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Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? by Pruthi, Danish, Bansal, Rachit, Dhingra, Bhuwan, Soares, Livio Baldini, Collins, Michael, Lipton, Zachary C., Neubig, Graham, Cohen, William W.

“…While many methods purport to predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go…”
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Improving neural machine translation through phrase-based soft forced decoding by Zhang, Jingyi, Utiyama, Masao, Sumita, Eiichro, Neubig, Graham, Nakamura, Satoshi

“…Compared to traditional statistical machine translation (SMT), such as phrase-based machine translation (PBMT), neural machine translation (NMT) often…”
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Postfilters to Modify the Modulation Spectrum for Statistical Parametric Speech Synthesis by Takamichi, Shinnosuke, Toda, Tomoki, Black, Alan W., Neubig, Graham, Sakti, Sakriani, Nakamura, Satoshi

“…This paper presents novel approaches based on modulation spectrum (MS) for high-quality statistical parametric speech synthesis, including text-to-speech (TTS)…”
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Neural Lattice Language Models by Buckman, Jacob, Neubig, Graham

“…In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple…”
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A comparative study of dictionaries and corpora as methods for language resource addition by Mori, Shinsuke, Neubig, Graham

“…In this paper, we investigate the relative effect of two strategies for language resource addition for Japanese morphological analysis, a joint task of word…”
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A postfilter to modify the modulation spectrum in HMM-based speech synthesis by Takamichi, Shinnosuke, Toda, Tomoki, Neubig, Graham, Sakti, Sakriani, Nakamura, Satoshi

“…In this paper, we propose a postfilter to compensate modulation spectrum in HMM-based speech synthesis. In order to alleviate over-smoothing effects which is a…”
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Linguistic Unit Discovery from Multi-Modal Inputs in Unwritten Languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop by Scharenborg, Odette, Besacier, Laurent, Black, Alan, Hasegawa-Johnson, Mark, Metze, Florian, Neubig, Graham, Stuker, Sebastian, Godard, Pierre, Muller, Markus, Ondel, Lucas, Palaskar, Shruti, Arthur, Philip, Ciannella, Francesco, Du, Mingxing, Larsen, Elin, Merkx, Danny, Riad, Rachid, Wang, Liming, Dupoux, Emmanuel

“…We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic…”
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Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation by Oda, Yusuke, Fudaba, Hiroyuki, Neubig, Graham, Hata, Hideaki, Sakti, Sakriani, Toda, Tomoki, Nakamura, Satoshi

“…Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source…”
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AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas by Kann, Katharina, Ebrahimi, Abteen, Mager, Manuel, Oncevay, Arturo, Ortega, John E, Rios, Annette, Fan, Angela, Gutierrez-Vasques, Ximena, Chiruzzo, Luis, Giménez-Lugo, Gustavo A, Ramos, Ricardo, Meza Ruiz, Ivan Vladimir, Mager, Elisabeth, Chaudhary, Vishrav, Neubig, Graham, Palmer, Alexis, Coto-Solano, Rolando, Vu, Ngoc Thang

“…Little attention has been paid to the development of human language technology for truly low-resource languages-i.e., languages with limited amounts of…”
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A monotonic statistical machine translation approach to speaking style transformation by Neubig, Graham, Akita, Yuya, Mori, Shinsuke, Kawahara, Tatsuya

“…► We present a method for transforming faithful/ASR transcripts to clean transcripts. ► This method is called “speaking style transformation.” ► We perform an…”
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