2 citations · 2 across the 4 of their papers we have counts for
5 papers
Learning a Grammar Inducer from Massive Uncurated Instructional Videos
Songyang Zhang, Linfeng Song, Lifeng Jin +4
Video-aided grammar induction aims to leverage video information for finding more accurate syntactic grammars for accompanying text. While previous work focuses on building systems…
Distant finetuning with discourse relations for stance classification
Lifeng Jin, Kun Xu, Linfeng Song +1
Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with ind…
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
Wenlin Yao, Xiaoman Pan, Lifeng Jin +3
Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictio…
Domain-Adaptive Pretraining Methods for Dialogue Understanding
Han Wu, Kun Xu, Linfeng Song +3
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adapt…
Video-aided Unsupervised Grammar Induction
Songyang Zhang, Linfeng Song, Lifeng Jin +3
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induc…