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Found 6 papers, 1 papers with code
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism
In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).
Inflected Forms Are Redundant in Question Generation Models
Secondly, we propose to adapt QG as a combination of the following actions in the encode-decoder framework: generating a question word, copying a word from the source sequence or generating a word transformation type.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model
To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance.
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models.
Answer-focused and Position-aware Neural Question Generation
(2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer.
