activity
20182026
most citedA Large-Scale Corpus for Conversation Disentanglement

91 citations · 92 across the 7 of their papers we have counts for

collaborators

7 papers

cs.HC2026

It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation

Sadra Sabouri, Zeinabsadat Saghi, Jordan Lee Boyd-Graber +3

Prior work on AI-assisted information evaluation has largely focused on what AI systems communicate, comparing explanation types and formats, with responses predominantly cast in d…

cs.CL2026

Your Students Don't Use LLMs Like You Wish They Did

Sebastian Kobler, Matthew Clemson, Angela Sun +1

Educational NLP systems are typically evaluated using engagement metrics and satisfaction surveys, which are at best a proxy for meeting pedagogical goals. We introduce six computa…

cs.HC2024

SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

Yuan Tian, Jonathan K. Kummerfeld, Toby Jia-Jun Li +1

Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain l…

cs.DB2023

Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations

Yuan Tian, Zheng Zhang, Zheng Ning +3

Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are…

cs.CL2022★ 1 cited

Augmenting Task-Oriented Dialogue Systems with Relation Extraction

Andrew Lee, Zhenguo Chen, Kevin Leach +1

The standard task-oriented dialogue pipeline uses intent classification and slot-filling to interpret user utterances. While this approach can handle a wide range of queries, it do…

cs.CL2021

Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks

Janarthanan Rajendran, Jonathan K. Kummerfeld, Satinder Singh

For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and t…

cs.CL2018★ 91 cited

A Large-Scale Corpus for Conversation Disentanglement

Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph Peper +6

Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new datase…