10 citations · 34 across the 6 of their papers we have counts for
6 papers
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Aaron Chan, Shaoliang Nie, Liang Tan +4
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained natu…
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
Aaron Chan, Maziar Sanjabi, Lambert Mathias +5
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationa…
Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea +1
Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue.…
AMR-to-text Generation with Synchronous Node Replacement Grammar
Linfeng Song, Xiaochang Peng, Yue Zhang +2
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic ext…
AMR-to-text generation as a Traveling Salesman Problem
Linfeng Song, Yue Zhang, Xiaochang Peng +2
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR grap…
Exploring phrase-compositionality in skip-gram models
Xiaochang Peng, Daniel Gildea
In this paper, we introduce a variation of the skip-gram model which jointly learns distributed word vector representations and their way of composing to form phrase embeddings. In…