SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding

• URL: http://arxiv.org/abs/2005.11347v1
• Date: Fri, 22 May 2020 18:32:35 GMT
• Title: SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
• Authors: Li Zhang, Han Wang, Lingxiao Li
• Abstract summary: We propose a unified locality weighting and learning framework to learn task-specific sentence embedding. Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair.
• Score: 12.020634125787279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pair-based metric learning has been widely adopted to learn sentence embedding in many NLP tasks such as semantic text similarity due to its efficiency in computation. Most existing works employed a sequence encoder model and utilized limited sentence pairs with a pair-based loss to learn discriminating sentence representation. However, it is known that the sentence representation can be biased when the sampled sentence pairs deviate from the true distribution of all sentence pairs. In this paper, our theoretical analysis shows that existing works severely suffered from a good pair sampling and instance weighting strategy. Instead of one time pair selection and learning on equal weighted pairs, we propose a unified locality weighting and learning framework to learn task-specific sentence embedding. Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair. Such weight is updated along with pair-loss optimization in each round, ensuring the model keep learning the most informative sentence pairs. Extensive experiments on four public available datasets and a self-collected place search benchmark with 1.4 million places clearly demonstrate that our model consistently outperforms existing sentence embedding methods with comparable efficiency.

Related papers

• Probabilistic Contrastive Learning for Long-Tailed Visual Recognition [78.70453964041718]
  Longtailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. We propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space.
  arXiv  Detail & Related papers  (2024-03-11T13:44:49Z)
• Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
  We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings. RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
  arXiv  Detail & Related papers  (2022-12-17T05:25:17Z)
• Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework [68.04940365847543]
  We propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data. Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data.
  arXiv  Detail & Related papers  (2022-10-30T10:15:21Z)
• Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity [30.647497555295974]
  We train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences. The distilled information from the classifier is then used to train a reliable sentence embedding model. Our model trained on synthetic data generalizes well and outperforms the existing baselines.
  arXiv  Detail & Related papers  (2022-08-29T05:42:22Z)
• Sentence Similarity Based on Contexts [31.135984064747607]
  The proposed framework is based on the core idea that the meaning of a sentence should be defined by its contexts. It is able to generate high-quality, large-scale dataset with semantic similarity scores between two sentences in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-05-17T06:03:56Z)
• Unsupervised Summarization by Jointly Extracting Sentences and Keywords [12.387378783627762]
  RepRank is an unsupervised graph-based ranking model for extractive multi-document summarization. We show that salient sentences and keywords can be extracted in a joint and mutual reinforcement process using our learned representations. Experiment results with multiple benchmark datasets show that RepRank achieved the best or comparable performance in ROUGE.
  arXiv  Detail & Related papers  (2020-09-16T05:58:00Z)
• Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble [163.3333439344695]
  Dirichlet Neighborhood Ensemble (DNE) is a randomized smoothing method for training a robust model to defense substitution-based attacks. DNE forms virtual sentences by sampling embedding vectors for each word in an input sentence from a convex hull spanned by the word and its synonyms, and it augments them with the training data. We demonstrate through extensive experimentation that our method consistently outperforms recently proposed defense methods by a significant margin across different network architectures and multiple data sets.
  arXiv  Detail & Related papers  (2020-06-20T18:01:16Z)
• Syntactic Structure Distillation Pretraining For Bidirectional Encoders [49.483357228441434]
  We introduce a knowledge distillation strategy for injecting syntactic biases into BERT pretraining. We distill the approximate marginal distribution over words in context from the syntactic LM. Our findings demonstrate the benefits of syntactic biases, even in representation learners that exploit large amounts of data.
  arXiv  Detail & Related papers  (2020-05-27T16:44:01Z)
• Fact-aware Sentence Split and Rephrase with Permutation Invariant Training [93.66323661321113]
  Sentence Split and Rephrase aims to break down a complex sentence into several simple sentences with its meaning preserved. Previous studies tend to address the issue by seq2seq learning from parallel sentence pairs. We introduce Permutation Training to verifies the effects of order variance in seq2seq learning for this task.
  arXiv  Detail & Related papers  (2020-01-16T07:30:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.