Search Results for author: Avirup Sil

Found 28 papers, 2 papers with code

MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

no code implementations20 Dec 2021 Revanth Gangi Reddy, Xilin Rui, Manling Li, Xudong Lin, Haoyang Wen, Jaemin Cho, Lifu Huang, Mohit Bansal, Avirup Sil, Shih-Fu Chang, Alexander Schwing, Heng Ji

Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.

Data Augmentation Question-Answer-Generation +1

Learning Cross-Lingual IR from an English Retriever

no code implementations15 Dec 2021 Yulong Li, Martin Franz, Md Arafat Sultan, Bhavani Iyer, Young-suk Lee, Avirup Sil

We present a new cross-lingual information retrieval (CLIR) model trained using multi-stage knowledge distillation (KD).

Information Retrieval Knowledge Distillation +2

Do Answers to Boolean Questions Need Explanations? Yes

no code implementations14 Dec 2021 Sara Rosenthal, Mihaela Bornea, Avirup Sil, Radu Florian, Scott McCarley

Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question.

On The Ingredients of an Effective Zero-shot Semantic Parser

no code implementations15 Oct 2021 Pengcheng Yin, John Wieting, Avirup Sil, Graham Neubig

Semantic parsers map natural language utterances into meaning representations (e. g., programs).

Semantic Parsing Zero-Shot Learning

Improved Text Classification via Contrastive Adversarial Training

no code implementations21 Jul 2021 Lin Pan, Chung-Wei Hang, Avirup Sil, Saloni Potdar, Mo Yu

We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks.

Contrastive Learning Intent Classification +2

VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension

no code implementations ACL 2021 Haoyang Wen, Anthony Ferritto, Heng Ji, Radu Florian, Avirup Sil

Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use.

Machine Reading Comprehension

Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering

no code implementations15 Apr 2021 Sara Rosenthal, Mihaela Bornea, Avirup Sil

Recent approaches have exploited weaknesses in monolingual question answering (QA) models by adding adversarial statements to the passage.

Question Answering

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

Towards Confident Machine Reading Comprehension

no code implementations20 Jan 2021 Rishav Chakravarti, Avirup Sil

Performance prediction is particularly important in cases of domain shift (as measured by training RC models on SQUAD 2. 0 and evaluating on NQ), where Mr. C not only improves AUC, but also traditional answerability prediction (as measured by a 5 point improvement in F1).

Machine Reading Comprehension Question Answering

Multilingual Transfer Learning for QA Using Translation as Data Augmentation

no code implementations10 Dec 2020 Mihaela Bornea, Lin Pan, Sara Rosenthal, Radu Florian, Avirup Sil

Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages.

Cross-Lingual Transfer Data Augmentation +4

Cross-lingual Structure Transfer for Relation and Event Extraction

no code implementations IJCNLP 2019 Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss

The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.

Event Extraction Relation Extraction

Ensembling Strategies for Answering Natural Questions

no code implementations30 Oct 2019 Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avirup Sil

Many of the top question answering systems today utilize ensembling to improve their performance on tasks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) challenges.

Question Answering

Structured Pruning of a BERT-based Question Answering Model

no code implementations14 Oct 2019 J. S. McCarley, Rishav Chakravarti, Avirup Sil

The recent trend in industry-setting Natural Language Processing (NLP) research has been to operate large %scale pretrained language models like BERT under strict computational limits.

Model Compression Question Answering

Frustratingly Easy Natural Question Answering

no code implementations11 Sep 2019 Lin Pan, Rishav Chakravarti, Anthony Ferritto, Michael Glass, Alfio Gliozzo, Salim Roukos, Radu Florian, Avirup Sil

Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.

Data Augmentation Question Answering +1

Span Selection Pre-training for Question Answering

1 code implementation ACL 2020 Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avirup Sil

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).

Language Modelling Question Answering +1

CFO: A Framework for Building Production NLP Systems

no code implementations IJCNLP 2019 Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avirup Sil

This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.

Information Retrieval Machine Reading Comprehension +1

Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking

no code implementations ACL 2018 Gourab Kundu, Avirup Sil, Radu Florian, Wael Hamza

We propose an entity-centric neural cross-lingual coreference model that builds on multi-lingual embeddings and language-independent features.

Coreference Resolution Entity Linking

Neural Cross-Lingual Entity Linking

no code implementations5 Dec 2017 Avirup Sil, Gourab Kundu, Radu Florian, Wael Hamza

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts.

Cross-Lingual Entity Linking Entity Disambiguation +3

One for All: Towards Language Independent Named Entity Linking

no code implementations ACL 2016 Avirup Sil, Radu Florian

Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc).

Entity Linking

Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures

no code implementations EMNLP 2017 Lifu Huang, Avirup Sil, Heng Ji, Radu Florian

Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities\_of\_residence) for a given entity from a large collection of source documents.

Relation Extraction Slot Filling

Toward Mention Detection Robustness with Recurrent Neural Networks

no code implementations24 Feb 2016 Thien Huu Nguyen, Avirup Sil, Georgiana Dinu, Radu Florian

One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages.

Named Entity Recognition Word Embeddings

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