Search Results for author:
Found 9 papers, 6 papers with code
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Very recently, a pre-trained generative retrieval model for KILTs, named CorpusBrain, was proposed and reached new state-of-the-art retrieval performance.
RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication.
Continual Learning for Generative Retrieval over Dynamic Corpora
We put forward a novel Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major contributions to continual learning for GR: (i) To encode new documents into docids with low computational cost, we present Incremental Product Quantization, which updates a partial quantization codebook according to two adaptive thresholds; and (ii) To memorize new documents for querying without forgetting previous knowledge, we propose a memory-augmented learning mechanism, to form meaningful connections between old and new documents.
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning.
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task.
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.
GERE: Generative Evidence Retrieval for Fact Verification
This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead; ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking; iii) a fixed number of sentences are selected to form the final evidence set.
FedMatch: Federated Learning Over Heterogeneous Question Answering Data
A possible solution to this dilemma is a new approach known as federated learning, which is a privacy-preserving machine learning technique over distributed datasets.
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets.
