Search Results for author:
Found 19 papers, 14 papers with code
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization
Specifically, FedMCP adds two lightweight adapter modules, i. e., the global adapter and the private adapter, to the frozen PLMs within clients.
Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
In this paper, we leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model.
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.
Exploring Dual Encoder Architectures for Question Answering
We further explore and explain why parameter sharing in projection layer significantly improves the efficacy of the dual encoders, by directly probing the embedding spaces of the two encoder towers with t-SNE algorithm.
Large Dual Encoders Are Generalizable Retrievers
With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization.
Ranked #9 on
Zero-shot Text Search
on BEIR
Passage Retrieval for Outside-Knowledge Visual Question Answering
We first conduct sparse retrieval with BM25 and study expanding the question with object names and image captions.
Natural Language Understanding with Privacy-Preserving BERT
We investigate the privacy and utility implications of applying dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU applications.
Weakly-Supervised Open-Retrieval Conversational Question Answering
Our method is more flexible as it can handle both span answers and freeform answers.
Open-Retrieval Conversational Question Answering
We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.
Attentive History Selection for Conversational Question Answering
First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
BERT with History Answer Embedding for Conversational Question Answering
One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
A Hybrid Retrieval-Generation Neural Conversation Model
In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.
Answer Interaction in Non-factoid Question Answering Systems
Information retrieval systems are evolving from document retrieval to answer retrieval.
User Intent Prediction in Information-seeking Conversations
Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.
Analyzing and Characterizing User Intent in Information-seeking Conversations
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.
