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Found 8 papers, 7 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.
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.
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.
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation
Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals.
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework.
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.
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
Precisely understanding users' contextual search intent has been an important challenge for conversational search.
