Search Results for author: Debjit Paul

Found 7 papers, 6 papers with code

Language Model Decoding as Likelihood-Utility Alignment

1 code implementation13 Oct 2022 Martin Josifoski, Maxime Peyrard, Frano Rajic, Jiheng Wei, Debjit Paul, Valentin Hartmann, Barun Patra, Vishrav Chaudhary, Emre Kiciman, Boi Faltings, Robert West

Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm.

Language Modelling Text Generation

Generating Hypothetical Events for Abductive Inference

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Debjit Paul, Anette Frank

This work offers the first study of how such knowledge impacts the Abductive NLI task -- which consists in choosing the more likely explanation for given observations.

Language Modelling

COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

1 code implementation ACL 2021 Debjit Paul, Anette Frank

Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque.

Story Completion

CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts

1 code implementation IWCS (ACL) 2021 Maria Becker, Katharina Korfhage, Debjit Paul, Anette Frank

We conduct evaluations on two argumentative datasets and show that a combination of the two model types generates meaningful, high-quality knowledge paths between sentences that reveal implicit knowledge conveyed in text.

Social Commonsense Reasoning with Multi-Head Knowledge Attention

1 code implementation Findings of the Association for Computational Linguistics 2020 Debjit Paul, Anette Frank

Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task.

Natural Language Inference

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

1 code implementation NAACL 2019 Debjit Paul, Anette Frank

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment.

Common Sense Reasoning

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