SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples

1 code implementation • 13 May 2023 • Deqing Fu, Ameya Godbole, Robin Jia

In this work, we propose Self-labeled Counterfactuals for Extrapolating to Negative Examples (SCENE), an automatic method for synthesizing training data that greatly improves models' ability to detect challenging negative examples.

Data Augmentation Natural Language Inference +2

Benchmarking Long-tail Generalization with Likelihood Splits

1 code implementation • 13 Oct 2022 • Ameya Godbole, Robin Jia

In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances.

Benchmarking Language Modelling +3

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

1 code implementation • 22 Feb 2022 • Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Robin Jia, Manzil Zaheer, Hannaneh Hajishirzi, Andrew McCallum

Question answering (QA) over knowledge bases (KBs) is challenging because of the diverse, essentially unbounded, types of reasoning patterns needed.

Knowledge Base Question Answering

Case-based Reasoning for Natural Language Queries over Knowledge Bases

no code implementations • EMNLP 2021 • Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay-Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum

It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions -- a paradigm known as case-based reasoning (CBR).

Knowledge Base Question Answering Natural Language Queries +1

Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum

A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem.

Knowledge Graph Completion Link Prediction +1

A Simple Approach to Case-Based Reasoning in Knowledge Bases

1 code implementation • AKBC 2020 • Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI).

Knowledge Graphs Meta-Learning +1

Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference

no code implementations • WS 2019 • Rajarshi Das, Ameya Godbole, Manzil Zaheer, Shehzaad Dhuliawala, Andrew McCallum

This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019).

Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering

no code implementations • WS 2019 • Ameya Godbole, Dilip Kavarthapu, Rajarshi Das, Zhiyu Gong, Abhishek Singhal, Hamed Zamani, Mo Yu, Tian Gao, Xiaoxiao Guo, Manzil Zaheer, Andrew McCallum

Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging.

Information Retrieval Multi-hop Question Answering +2

Siamese Neural Networks with Random Forest for detecting duplicate question pairs

no code implementations • 22 Jan 2018 • Ameya Godbole, Aman Dalmia, Sunil Kumar Sahu

We got the best result by using the Siamese adaptation of a Bidirectional GRU with a Random Forest classifier, which landed us among the top 24% in the competition Quora Question Pairs hosted on Kaggle.

BIG-bench Machine Learning