Learning sentence representations which capture
rich semantic meanings has been crucial
for many NLP tasks. Pre-trained language
models such as BERT have achieved great
success in NLP, but sentence embeddings extracted
directly from these models do not perform
well without fine-tuning. We propose
Contrastive Learning of Sentence Representations
(CLSR), a novel approach which applies
contrastive learning to learn universal sentence
representations on top of pre-trained language
models. CLSR utilizes semantic similarity of
two sentences to construct positive instance
for contrastive learning. Semantic information
that has been captured by the pre-trained
models is kept by getting sentence embeddings
from these models with proper pooling strategy.
An encoder followed by a linear projection
takes these embeddings as inputs and is
trained under a contrastive objective. To evaluate
the performance of CLSR, we run experiments
on a range of pre-trained language models
and their variants on a series of Semantic
Contextual Similarity tasks. Results show that
CLSR gains significant performance improvements
over existing SOTA language models.
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Delta Embedding Learning
Unsupervised word embeddings have become a popular approach of word representation in NLP tasks. However there are limitations to the semantics represented by unsupervised embeddings, and inadequate fine-tuning of embeddings can lead to suboptimal performance. We propose a novel learning technique called Delta Embedding Learning, which can be applied to general NLP tasks to improve performance by optimized tuning of the word embeddings. A structured regularization is applied to the embeddings to ensure they are tuned in an incremental way. As a result, the tuned word embeddings become better word representations by absorbing semantic information from supervision without “forgetting.” We apply the method to various NLP tasks and see a consistent improvement in performance. Evaluation also confirms the tuned word embeddings have better semantic properties.
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- Award ID(s):
- 1747798
- PAR ID:
- 10131160
- Date Published:
- Journal Name:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Page Range / eLocation ID:
- 3329 to 3334
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/P19-1322
