276 citations · 2k across the 138 of their papers we have counts for
19 papers · 2 filters
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
Jifan Chen, Yuhao Zhang, Lan Liu +5
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022). However, existing met…
CausalDialogue: Modeling Utterance-level Causality in Conversations
Yi-Lin Tuan, Alon Albalak, Wenda Xu +4
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and…
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
Kaiser Sun, Peng Qi, Yuhao Zhang +3
Generative models have been widely applied to solve extractive tasks, where parts of the input is extracted to form the desired output, and achieved significant success. For exampl…
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI
Alex Mei, Sharon Levy, William Yang Wang
Users' physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can…
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
Wenda Xu, Xian Qian, Mingxuan Wang +2
Is it possible to train a general metric for evaluating text generation quality without human annotated ratings? Existing learned metrics either perform unsatisfactorily across tex…
WikiWhy: Answering and Explaining Cause-and-Effect Questions
Matthew Ho, Aditya Sharma, Justin Chang +4
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (…
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Wenda Xu, Yilin Tuan, Yujie Lu +3
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric? Existing learned metrics either perform unsatisfactorily or are restricted to t…
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Gyuwan Kim, Jinhyuk Lee, Barlas Oguz +4
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these proced…
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding
Josiah Ross, Luke Yoffe, Alon Albalak +1
Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency. This study explores the…
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Sharon Levy, Emily Allaway, Melanie Subbiah +4
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe. One such typ…
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
An Yan, Jiacheng Li, Wanrong Zhu +3
Contrastive Language-Image Pretraining (CLIP) efficiently learns visual concepts by pre-training with natural language supervision. CLIP and its visual encoder have been explored o…
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
Zhiyu Chen, Shiyang Li, Charese Smiley +3
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The communi…
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Wanrong Zhu, An Yan, Yujie Lu +4
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context. On the other hand, when generating text, human writers are gifted…
Neuro-Symbolic Procedural Planning with Commonsense Prompting
Yujie Lu, Weixi Feng, Wanrong Zhu +4
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Although procedural planning is a basic skill set for human…
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation
Samhita Honnavalli, Aesha Parekh, Lily Ou +4
Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natur…
KETOD: Knowledge-Enriched Task-Oriented Dialogue
Zhiyu Chen, Bing Liu, Seungwhan Moon +3
Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse natura…
Imagination-Augmented Natural Language Understanding
Yujie Lu, Wanrong Zhu, Xin Eric Wang +2
Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations. Such abilities ena…
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi +6
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue underst…
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems
Yi-Lin Tuan, Sajjad Beygi, Maryam Fazel-Zarandi +3
Users interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response. This limits the user experience, and is par…