Search Results for author:
Found 12 papers, 4 papers with code
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation
Chinese word segmentation (CWS) is an essential task for Chinese downstream NLP tasks.
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision
Besides, the robustness of the previous neural methods is limited by the large-scale annotated data.
DUTNLP Machine Translation System for WMT21 Triangular Translation Task
This paper describes DUT-NLP Lab’s submission to the WMT-21 triangular machine translation shared task.
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
Context-Aware Word Segmentation for Chinese Real-World Discourse
This paper proposes a three-step strategy to improve the performance for discourse CWS.
ConvSDG: Session Data Generation for Conversational Search
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine.
Improving Cross-lingual Representation for Semantic Retrieval with Code-switching
Semantic Retrieval (SR) has become an indispensable part of the FAQ system in the task-oriented question-answering (QA) dialogue scenario.
History-Aware Conversational Dense Retrieval
To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.
Learning to Relate to Previous Turns in Conversational Search
An effective way to improve retrieval effectiveness is to expand the current query with historical queries.
ConvGQR: Generative Query Reformulation for Conversational Search
In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers.
