1,839 research outputs found
Multilingual Universal Sentence Encoder for Semantic Retrieval
We introduce two pre-trained retrieval focused multilingual sentence encoding
models, respectively based on the Transformer and CNN model architectures. The
models embed text from 16 languages into a single semantic space using a
multi-task trained dual-encoder that learns tied representations using
translation based bridge tasks (Chidambaram al., 2018). The models provide
performance that is competitive with the state-of-the-art on: semantic
retrieval (SR), translation pair bitext retrieval (BR) and retrieval question
answering (ReQA). On English transfer learning tasks, our sentence-level
embeddings approach, and in some cases exceed, the performance of monolingual,
English only, sentence embedding models. Our models are made available for
download on TensorFlow Hub.Comment: 6 pages, 6 tables, 2 listings, and 1 figur
Dense Text Retrieval based on Pretrained Language Models: A Survey
Text retrieval is a long-standing research topic on information seeking,
where a system is required to return relevant information resources to user's
queries in natural language. From classic retrieval methods to learning-based
ranking functions, the underlying retrieval models have been continually
evolved with the ever-lasting technical innovation. To design effective
retrieval models, a key point lies in how to learn the text representation and
model the relevance matching. The recent success of pretrained language models
(PLMs) sheds light on developing more capable text retrieval approaches by
leveraging the excellent modeling capacity of PLMs. With powerful PLMs, we can
effectively learn the representations of queries and texts in the latent
representation space, and further construct the semantic matching function
between the dense vectors for relevance modeling. Such a retrieval approach is
referred to as dense retrieval, since it employs dense vectors (a.k.a.,
embeddings) to represent the texts. Considering the rapid progress on dense
retrieval, in this survey, we systematically review the recent advances on
PLM-based dense retrieval. Different from previous surveys on dense retrieval,
we take a new perspective to organize the related work by four major aspects,
including architecture, training, indexing and integration, and summarize the
mainstream techniques for each aspect. We thoroughly survey the literature, and
include 300+ related reference papers on dense retrieval. To support our
survey, we create a website for providing useful resources, and release a code
repertory and toolkit for implementing dense retrieval models. This survey aims
to provide a comprehensive, practical reference focused on the major progress
for dense text retrieval
Information Retrieval: Recent Advances and Beyond
In this paper, we provide a detailed overview of the models used for
information retrieval in the first and second stages of the typical processing
chain. We discuss the current state-of-the-art models, including methods based
on terms, semantic retrieval, and neural. Additionally, we delve into the key
topics related to the learning process of these models. This way, this survey
offers a comprehensive understanding of the field and is of interest for for
researchers and practitioners entering/working in the information retrieval
domain
Zero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model
We present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint multilingual space and learns to distinguish true translation pairs from semantically related sentences across languages. The proposed model combines a recurrent sequence encoder with a bidirectional attention layer and an intra-sentence attention mechanism. This way the final fixed-size sentence representations in each training sentence pair depend on the selection of contextualized token representations from the other sentence. The representations of both sentences are then combined using the bilinear product function to predict the relevance score. We show that, coupled with a shared
multilingual word embedding space, the proposed model strongly outperforms unsupervised cross-lingual ranking functions, and that further boosts can be achieved by combining the two approaches. Most importantly, we demonstrate the model's effectiveness in zero-shot language transfer settings: our multilingual framework boosts cross-lingual sentence retrieval performance for unseen language pairs without any training examples. This enables robust cross-lingual sentence retrieval
also for pairs of resource-lean languages, without any parallel data
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