12 citations · 54 across the 14 of their papers we have counts for
13 papers · 1 filter
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge
Kaixin Ma, Hao Cheng, Xiaodong Liu +2
We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is…
Table Retrieval May Not Necessitate Table-specific Model Design
Zhiruo Wang, Zhengbao Jiang, Eric Nyberg +1
Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent wo…
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models
Kaixin Ma, Filip Ilievski, Jonathan Francis +3
Commonsense reasoning benchmarks have been largely solved by fine-tuning language models. The downside is that fine-tuning may cause models to overfit to task-specific data and the…
Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection
Yikang Li, Pulkit Goel, Varsha Kuppur Rajendra +5
Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models. In this work, we specifically focus on the Commonge…
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
Kaixin Ma, Filip Ilievski, Jonathan Francis +3
Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that mo…
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Kaixin Ma, Jonathan Francis, Quanyang Lu +2
Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate re…