A survey of data augmentation approaches for NLP
Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …
resource domains, new tasks, and the popularity of large-scale neural networks that require …
[图书][B] Pretrained transformers for text ranking: Bert and beyond
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …
response to a query. Although the most common formulation of text ranking is search …
Dense passage retrieval for open-domain question answering
Open-domain question answering relies on efficient passage retrieval to select candidate
contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de …
contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de …
PAQ: 65 million probably-asked questions and what you can do with them
Abstract Open-domain Question Answering models that directly leverage question-answer
(QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in …
(QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in …
Improving bert performance for aspect-based sentiment analysis
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market
products. It involves examining the type of sentiments as well as sentiment targets …
products. It involves examining the type of sentiments as well as sentiment targets …
What do models learn from question answering datasets?
While models have reached superhuman performance on popular question answering (QA)
datasets such as SQuAD, they have yet to outperform humans on the task of question …
datasets such as SQuAD, they have yet to outperform humans on the task of question …
Machine reading comprehension: The role of contextualized language models and beyond
Machine reading comprehension (MRC) aims to teach machines to read and comprehend
human languages, which is a long-standing goal of natural language processing (NLP) …
human languages, which is a long-standing goal of natural language processing (NLP) …
Interactive question answering systems: Literature review
Question-answering systems are recognized as popular and frequently effective means of
information seeking on the web. In such systems, information seekers can receive a concise …
information seeking on the web. In such systems, information seekers can receive a concise …
Are neural ranking models robust?
Recently, we have witnessed the bloom of neural ranking models in the information retrieval
(IR) field. So far, much effort has been devoted to developing effective neural ranking …
(IR) field. So far, much effort has been devoted to developing effective neural ranking …
Cross-lingual machine reading comprehension
Though the community has made great progress on Machine Reading Comprehension
(MRC) task, most of the previous works are solving English-based MRC problems, and there …
(MRC) task, most of the previous works are solving English-based MRC problems, and there …