Search Results for author: Chaitanya Malaviya

Found 15 papers, 12 papers with code

Pachinko: Patching Interpretable QA Models through Natural Language Feedback

1 code implementation16 Nov 2023 Chaitanya Malaviya, Subin Lee, Dan Roth, Mark Yatskar

In the first one, we present users with incorrect answers and corresponding rationales of various formats and ask them to provide natural language feedback to revise the rationale.

Question Answering Reading Comprehension

ExpertQA: Expert-Curated Questions and Attributed Answers

2 code implementations14 Sep 2023 Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber, Mark Yatskar, Dan Roth

As language models are adapted by a more sophisticated and diverse set of users, the importance of guaranteeing that they provide factually correct information supported by verifiable sources is critical across fields of study & professions.

Language Modelling

QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations

1 code implementation19 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.

Natural Language Queries Retrieval

AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference

1 code implementation1 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.


Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models

1 code implementation24 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.

Multiple-choice Reading Comprehension

The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection

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.

Cross-Lingual Transfer Lemmatization +3

Commonsense Knowledge Base Completion with Structural and Semantic Context

1 code implementation7 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.

Knowledge Base Completion Knowledge Graphs +3

COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

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).

graph construction Knowledge Graphs

DyNet: The Dynamic Neural Network Toolkit

4 code implementations15 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.

graph construction

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