398 citations · 2.8k across the 98 of their papers we have counts for
8 papers · 2 filters
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision
Trapit Bansal, Pat Verga, Neha Choudhary +1
Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonic…
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition
Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan +2
Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, includin…
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Trapit Bansal, Rishikesh Jha, Andrew McCallum
Self-supervised pre-training of transformer models has shown enormous success in improving performance on a number of downstream tasks. However, fine-tuning on a new task still req…
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Ameya Godbole, Dilip Kavarthapu, Rajarshi Das +8
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 retrieva…
Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell, Ananya Ganesh, Andrew McCallum
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained nota…
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer +1
This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to th…
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Sheshera Mysore, Zach Jensen, Edward Kim +6
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures…
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Andrew Drozdov, Pat Verga, Mohit Yadav +2
We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents with…