4 code implementations • 15 Jan 2017 • Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
2 code implementations • EMNLP 2017 • Chaitanya Malaviya, Graham Neubig, Patrick Littell
One central mystery of neural NLP is what neural models "know" about their subject matter.
no code implementations • ACL 2018 • Chaitanya Malaviya, Matthew R. Gormley, Graham Neubig
Morphological analysis involves predicting the syntactic traits of a word (e. g. {POS: Noun, Case: Acc, Gender: Fem}).
1 code implementation • ACL 2018 • Chaitanya Malaviya, Pedro Ferreira, André F. T. Martins
In NMT, words are sometimes dropped from the source or generated repeatedly in the translation.
no code implementations • NAACL 2019 • Chaitanya Malaviya, Shijie Wu, Ryan Cotterell
English verbs have multiple forms.
1 code implementation • ACL 2019 • Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi
We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).
2 code implementations • ICLR 2020 • Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen-tau Yih, Yejin Choi
Abductive reasoning is inference to the most plausible explanation.
1 code implementation • 7 Oct 2019 • Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, Yejin Choi
Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.
no code implementations • WS 2019 • Arya D. McCarthy, Ekaterina Vylomova, Shijie Wu, Chaitanya Malaviya, Lawrence Wolf-Sonkin, Garrett Nicolai, Christo Kirov, Miikka Silfverberg, Sabrina J. Mielke, Jeffrey Heinz, Ryan Cotterell, Mans Hulden
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance.
Ranked #1 on Question Answering on CODAH
1 code implementation • 24 Oct 2022 • Chaitanya Malaviya, Sudeep Bhatia, Mark Yatskar
Cognitive psychologists have documented that humans use cognitive heuristics, or mental shortcuts, to make quick decisions while expending less effort.
1 code implementation • 1 Feb 2023 • Yuewei Yuan, Chaitanya Malaviya, Mark Yatskar
To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents.
1 code implementation • 19 May 2023 • Chaitanya Malaviya, Peter Shaw, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
To study the ability of retrieval systems to meet such information needs, we construct QUEST, a dataset of 3357 natural language queries with implicit set operations, that map to a set of entities corresponding to Wikipedia documents.
2 code implementations • 14 Sep 2023 • Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber, Mark Yatskar, Dan Roth
In this work, we conduct human evaluation of responses from a few representative systems along various axes of attribution and factuality, by bringing domain experts in the loop.
2 code implementations • 16 Nov 2023 • Chaitanya Malaviya, Subin Lee, Dan Roth, Mark Yatskar
A rationale outlines the approach followed by the model to answer the question.
no code implementations • 21 Feb 2024 • Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang, Yanai Elazar, Niket Tandon, Marianna Apidianaki, Mrinmaya Sachan, Chris Callison-Burch
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.