33 citations · 98 across the 32 of their papers we have counts for
9 papers · 1 filter
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons
Pei Zhou, Andrew Zhu, Jennifer Hu +5
We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D&D), a role-playing game, pro…
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality
Pei Zhou, Hyundong Cho, Pegah Jandaghi +4
Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demo…
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models
Dong-Ho Lee, Akshen Kadakia, Brihi Joshi +8
NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging ai…
Does Wikidata Support Analogical Reasoning?
Filip Ilievski, Jay Pujara, Kartik Shenoy
Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination. While the wide…
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model
Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen +2
In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre-trai…
Understanding Narratives through Dimensions of Analogy
Thiloshon Nagarajah, Filip Ilievski, Jay Pujara
Analogical reasoning is a powerful qualitative reasoning tool that enables humans to connect two situations, and to generalize their knowledge from familiar to novel situations. Co…
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…
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer
Woojeong Jin, Dong-Ho Lee, Chenguang Zhu +2
Pre-trained language models are still far from human performance in tasks that need understanding of properties (e.g. appearance, measurable quantity) and affordances of everyday o…
Evaluating Machine Common Sense via Cloze Testing
Ehsan Qasemi, Lee Kezar, Jay Pujara +1
Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open quest…