activity
20182023
most citedXLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition

8 citations · 10 across the 4 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL20201 cited

Multiplex Word Embeddings for Selectional Preference Acquisition

Hongming Zhang, Jiaxin Bai, Yan Song +5

Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such repr…

cs.CL2019

What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues

Xintong Yu, Hongming Zhang, Yangqiu Song +2

Grounding a pronoun to a visual object it refers to requires complex reasoning from various information sources, especially in conversational scenarios. For example, when people in…

cs.CL20191 cited

Knowledge-aware Pronoun Coreference Resolution

Hongming Zhang, Yan Song, Yangqiu Song +1

Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge…

cs.CL2018

A Joint Model of Conversational Discourse and Latent Topics on Microblogs

Jing Li, Yan Song, Zhongyu Wei +1

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts resul…

cs.CL2018

Topic Memory Networks for Short Text Classification

Jichuan Zeng, Jing Li, Yan Song +3

Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic…

cs.CL2018

Iterative Document Representation Learning Towards Summarization with Polishing

Xiuying Chen, Shen Gao, Chongyang Tao +3

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often…