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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization

Shruti PalaskarAkshita BhagiaYonatan BiskAna Marasović
2022
Findings of EMNLP

Integrating vision and language has gained no-table attention following the success of pretrained language models. Despite that, a fraction of emerging multimodal models is suitable for text… 

Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection

Luca Di LielloSiddhant GargLuca SoldainiAlessandro Moschitti
2022
EMNLP

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… 

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Yizhong WangSwaroop MishraPegah AlipoormolabashiDaniel Khashabi
2022
EMNLP

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce SUPER-NATURALINSTRUCTIONS, a benchmark of 1,616… 

Teaching Broad Reasoning Skills via Decomposition-Guided Contexts

Harsh TrivediNiranjan BalasubramanianTushar KhotAshish Sabharwal
2022
EMNLP

Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion.… 

Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement

Bhavana Dalvi MishraOyvind TafjordPeter Clark
2022
EMNLP

Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. Our… 

Twist Decoding: Diverse Generators Guide Each Other

Jungo KasaiKeisuke SakaguchiRonan Le BrasNoah A. Smith
2022
EMNLP

Natural language generation technology has recently seen remarkable progress with large-scale training, and many natural language applications are now built upon a wide range of generation models.… 

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Tianbao XieChen Henry WuPeng ShiTao Yu
2022
EMNLP

Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs… 

Unsupervised Learning of Hierarchical Conversation Structure

Bo-Ru LuYushi HuHao ChengMari Ostendorf
2022
EMNLP Findings

Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure,… 

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

Alisa LiuSwabha SwayamdiptaNoah A. SmithYejin Choi
2022
Findings of EMNLP

A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a… 

What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment

Matthew FinlaysonKyle RichardsonAshish SabharwalPeter Clark
2022
EMNLP

The instruction learning paradigm—where a model learns to perform new tasks from task descriptions alone—has become popular in general-purpose model research. The capabilities of large transformer…