Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need…
Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication
Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer…
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website…
Exploring the Challenges of Open Domain Multi-Document Summarization
Multi-document summarization (MDS) has traditionally been studied assuming a set of ground-truth topic-related input documents is provided. In practice, the input document set is unlikely to be…
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Pre-trained language models, despite their rapid advancements powered by scale, still fall short of robust commonsense capabilities. And yet, scale appears to be the win-ning recipe; after all, the…
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pa-per, we explore…
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on pro-ducing consistent…
Knowledge Transfer from Answer Ranking to Answer Generation
Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This…
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
An important task for designing QA systems is answer sentence selection (AS2): select-ing the sentence containing (or constituting) the answer to a question from a set of re-trieved relevant…
Cross-Lingual GenQA: Open-Domain Question Answering with Answer Sentence Generation
Recent approaches for question answering systems have achieved impressive performance on English by combining document-level retrieval with answer generation. These approaches, which we refer to as…