1,045 research outputs found
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, instances of the task can also be found in many natural
language processing applications. This survey provides an overview of text
ranking with neural network architectures known as transformers, of which BERT
is the best-known example. The combination of transformers and self-supervised
pretraining has been responsible for a paradigm shift in natural language
processing (NLP), information retrieval (IR), and beyond. In this survey, we
provide a synthesis of existing work as a single point of entry for
practitioners who wish to gain a better understanding of how to apply
transformers to text ranking problems and researchers who wish to pursue work
in this area. We cover a wide range of modern techniques, grouped into two
high-level categories: transformer models that perform reranking in multi-stage
architectures and dense retrieval techniques that perform ranking directly.
There are two themes that pervade our survey: techniques for handling long
documents, beyond typical sentence-by-sentence processing in NLP, and
techniques for addressing the tradeoff between effectiveness (i.e., result
quality) and efficiency (e.g., query latency, model and index size). Although
transformer architectures and pretraining techniques are recent innovations,
many aspects of how they are applied to text ranking are relatively well
understood and represent mature techniques. However, there remain many open
research questions, and thus in addition to laying out the foundations of
pretrained transformers for text ranking, this survey also attempts to
prognosticate where the field is heading
Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker
Retrieval with extremely long queries and documents is a well-known and
challenging task in information retrieval and is commonly known as
Query-by-Document (QBD) retrieval. Specifically designed Transformer models
that can handle long input sequences have not shown high effectiveness in QBD
tasks in previous work. We propose a Re-Ranker based on the novel Proportional
Relevance Score (RPRS) to compute the relevance score between a query and the
top-k candidate documents. Our extensive evaluation shows RPRS obtains
significantly better results than the state-of-the-art models on five different
datasets. Furthermore, RPRS is highly efficient since all documents can be
pre-processed, embedded, and indexed before query time which gives our
re-ranker the advantage of having a complexity of O(N) where N is the total
number of sentences in the query and candidate documents. Furthermore, our
method solves the problem of the low-resource training in QBD retrieval tasks
as it does not need large amounts of training data, and has only three
parameters with a limited range that can be optimized with a grid search even
if a small amount of labeled data is available. Our detailed analysis shows
that RPRS benefits from covering the full length of candidate documents and
queries.Comment: Accepted at ACM Transactions on Information Systems (ACM TOIS
journal
PARADE: Passage Representation Aggregation for Document Reranking
We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking. PARADE leverages passage-level relevance representations to predict a document relevance score, overcoming the limitations of previous approaches that perform inference on passages independently. Experiments on two ad-hoc retrieval benchmarks demonstrate PARADE's effectiveness over such methods. We conduct extensive analyses on PARADE's efficiency, highlighting several strategies for improving it. When combined with knowledge distillation, a PARADE model with 72\% fewer parameters achieves effectiveness competitive with previous approaches using BERT-Base. Our code is available at \url{https://github.com/canjiali/PARADE}
CEQE: Contextualized Embeddings for Query Expansion
In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextualized Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized embedding vectors. We study the behavior of contextual representations generated for query expansion in ad-hoc document retrieval. We conduct our experiments on probabilistic retrieval models as well as in combination with neural ranking models. We evaluate CEQE on two standard TREC collections: Robust and Deep Learning. We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. We further find that multiple passes of expansion and reranking result in continued gains in effectiveness with CEQE-based approaches outperforming other approaches. The final model incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
- …