Search Results for author: Vikas Yadav

Found 13 papers, 7 papers with code

Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading

1 code implementation8 Jun 2021 Hoang Van, Vikas Yadav, Mihai Surdeanu

We propose a simple and effective strategy for data augmentation for low-resource machine reading comprehension (MRC).

Data Augmentation Machine Reading Comprehension +1

If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering

no code implementations NAACL 2021 Vikas Yadav, Steven Bethard, Mihai Surdeanu

We specifically emphasize on the importance of retrieving evidence jointly by showing several comparative analyses to other methods that retrieve and rerank evidence sentences individually.

Answer Selection Multi-hop Question Answering +1

Towards Robust Neural Retrieval Models with Synthetic Pre-Training

no code implementations15 Apr 2021 Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, Avirup Sil

Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems.

Information Retrieval Machine Reading Comprehension +1

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

1 code implementation ACL 2020 Vikas Yadav, Steven Bethard, Mihai Surdeanu

Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method.

Evidence Selection Multi-hop Question Answering +2

Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

no code implementations IJCNLP 2019 Vikas Yadav, Steven Bethard, Mihai Surdeanu

We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC).

Information Retrieval Multi-hop Question Answering +3

A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

1 code implementation COLING 2018 Vikas Yadav, Steven Bethard

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc.

Feature Engineering Information Retrieval +5

Alignment over Heterogeneous Embeddings for Question Answering

1 code implementation NAACL 2019 Vikas Yadav, Steven Bethard, Mihai Surdeanu

We propose a simple, fast, and mostly-unsupervised approach for non-factoid question answering (QA) called Alignment over Heterogeneous Embeddings (AHE).

Question Answering Sentence Embeddings

Deep Affix Features Improve Neural Named Entity Recognizers

1 code implementation SEMEVAL 2018 Vikas Yadav, Rebecca Sharp, Steven Bethard

We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word.

Feature Engineering Morphological Analysis +2

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