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Found 22 papers, 19 papers with code
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many information retrieval benchmarks.
Multi-Vector Retrieval as Sparse Alignment
With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval.
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search.
Optimizing Test-Time Query Representations for Dense Retrieval
In this paper, we introduce TOUR (Test-Time Optimization of Query Representations), which further optimizes instance-level query representations guided by signals from test-time retrieval results.
BERN2: an advanced neural biomedical named entity recognition and normalization tool
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e. g. diseases and drugs) from the ever-growing biomedical literature.
Simple Questions Generate Named Entity Recognition Datasets
Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities.
Simple Entity-Centric Questions Challenge Dense Retrievers
Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples.
Ranked #3 on
Passage Retrieval
on EntityQuestions
Phrase Retrieval Learns Passage Retrieval, Too
Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems.
Can Language Models be Biomedical Knowledge Bases?
To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs.
Learning Dense Representations of Phrases at Scale
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
Ranked #1 on
Question Answering
on Natural Questions (long)
Answering Questions on COVID-19 in Real-Time
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it.
Biomedical Entity Representations with Synonym Marginalization
In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates.
Look at the First Sentence: Position Bias in Question Answering
In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e. g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.
Adversarial Subword Regularization for Robust Neural Machine Translation
Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates.
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.
Pre-trained Language Model for Biomedical Question Answering
The recent success of question answering systems is largely attributed to pre-trained language models.
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Typeface Completion with Generative Adversarial Networks
However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese.
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source.
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types.
Ranked #13 on
Named Entity Recognition (NER)
on BC5CDR
Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior.
