40,653 research outputs found
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextualized language model BERT for the task of ad hoc table
retrieval. We investigate how to encode table content considering the table
structure and input length limit of BERT. We also propose an approach that
incorporates features from prior literature on table retrieval and jointly
trains them with BERT. In experiments on public datasets, we show that our best
approach can outperform the previous state-of-the-art method and BERT baselines
with a large margin under different evaluation metrics.Comment: Accepted at SIGIR 2020 (Long
CEDR: Contextualized Embeddings for Document Ranking
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language modes (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models
CEDR: Contextualized Embeddings for Document Ranking
Although considerable attention has been given to neural ranking
architectures recently, far less attention has been paid to the term
representations that are used as input to these models. In this work, we
investigate how two pretrained contextualized language models (ELMo and BERT)
can be utilized for ad-hoc document ranking. Through experiments on TREC
benchmarks, we find that several existing neural ranking architectures can
benefit from the additional context provided by contextualized language models.
Furthermore, we propose a joint approach that incorporates BERT's
classification vector into existing neural models and show that it outperforms
state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR
(Contextualized Embeddings for Document Ranking). We also address practical
challenges in using these models for ranking, including the maximum input
length imposed by BERT and runtime performance impacts of contextualized
language models.Comment: Appeared in SIGIR 2019, 4 page
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Neural networks provide new possibilities to automatically learn complex
language patterns and query-document relations. Neural IR models have achieved
promising results in learning query-document relevance patterns, but few
explorations have been done on understanding the text content of a query or a
document. This paper studies leveraging a recently-proposed contextual neural
language model, BERT, to provide deeper text understanding for IR. Experimental
results demonstrate that the contextual text representations from BERT are more
effective than traditional word embeddings. Compared to bag-of-words retrieval
models, the contextual language model can better leverage language structures,
bringing large improvements on queries written in natural languages. Combining
the text understanding ability with search knowledge leads to an enhanced
pre-trained BERT model that can benefit related search tasks where training
data are limited.Comment: In proceedings of SIGIR 201
Contextualized Word Representations for Reading Comprehension
Reading a document and extracting an answer to a question about its content
has attracted substantial attention recently. While most work has focused on
the interaction between the question and the document, in this work we evaluate
the importance of context when the question and document are processed
independently. We take a standard neural architecture for this task, and show
that by providing rich contextualized word representations from a large
pre-trained language model as well as allowing the model to choose between
context-dependent and context-independent word representations, we can obtain
dramatic improvements and reach performance comparable to state-of-the-art on
the competitive SQuAD dataset.Comment: 6 pages, 1 figure, NAACL 201
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in the matching process. To tackle this problem, we present a new
deep architecture to match two sentences with multiple positional sentence
representations. Specifically, each positional sentence representation is a
sentence representation at this position, generated by a bidirectional long
short term memory (Bi-LSTM). The matching score is finally produced by
aggregating interactions between these different positional sentence
representations, through -Max pooling and a multi-layer perceptron. Our
model has several advantages: (1) By using Bi-LSTM, rich context of the whole
sentence is leveraged to capture the contextualized local information in each
positional sentence representation; (2) By matching with multiple positional
sentence representations, it is flexible to aggregate different important
contextualized local information in a sentence to support the matching; (3)
Experiments on different tasks such as question answering and sentence
completion demonstrate the superiority of our model.Comment: Accepted by AAAI-201
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