Search Results for author:
Found 17 papers, 7 papers with code
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification
Question Answering (QA) systems aim to return correct and concise answers in response to user questions.
A Zero-Shot Claim Detection Framework Using Question Answering
In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection.
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA
In this work, we propose a method for enabling LLMs to decide when to retrieve in RAG settings given a conversational context.
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization
In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains.
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences.
Background Summarization of Event Timelines
Generating concise summaries of news events is a challenging natural language processing task.
SumREN: Summarizing Reported Speech about Events in News
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i. e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i. e., reported statements).
PLAtE: A Large-scale Dataset for List Page Web Extraction
Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites.
Answer Consolidation: Formulation and Benchmarking
Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer.
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers.
Summary-Oriented Question Generation for Informational Queries
Our model shows SOTA performance of SQ generation on the NQ dataset (20. 1 BLEU-4).
Inverse Reinforcement Learning with Natural Language Goals
In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a language-conditioned policy and reward function.
Fluent Response Generation for Conversational Question Answering
In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
Multi-domain dialogue state tracking (DST) is a critical component for conversational AI systems.
Ranked #9 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Active Learning in Recommendation Systems with Multi-level User Preferences
While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences.
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL).
