Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence

• URL: http://arxiv.org/abs/2110.07160v1
• Date: Thu, 14 Oct 2021 05:26:39 GMT
• Title: Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
• Authors: Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, Wray Buntine
• Abstract summary: It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. Our experiments show that Transformer$2$ manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure.
• Score: 6.73258176462356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a transformer over transformer framework, called Transformer$^2$, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learned from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformer$^2$ can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformer$^2$ manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.

Related papers

• Enhanced Transformer Architecture for Natural Language Processing [2.6071653283020915]
  Transformer is a state-of-the-art model in the field of natural language processing (NLP) In this paper, a novel structure of Transformer is proposed. It is featured by full layer normalization, weighted residual connection, positional encoding exploiting reinforcement learning, and zero masked self-attention. The proposed Transformer model, which is called Enhanced Transformer, is validated by the bilingual evaluation understudy (BLEU) score obtained with the Multi30k translation dataset.
  arXiv  Detail & Related papers  (2023-10-17T01:59:07Z)
• GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation [107.2752114891855]
  Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. We propose the Group-Transformer model (GTrans) that flexibly divides multi-layer representations of both encoder and decoder into different groups and then fuses these group features to generate target words.
  arXiv  Detail & Related papers  (2022-07-29T04:10:36Z)
• Paragraph-based Transformer Pre-training for Multi-Sentence Inference [99.59693674455582]
  We show that popular pre-trained transformers perform poorly when used for fine-tuning on multi-candidate inference tasks. We then propose a new pre-training objective that models the paragraph-level semantics across multiple input sentences.
  arXiv  Detail & Related papers  (2022-05-02T21:41:14Z)
• Sentence Bottleneck Autoencoders from Transformer Language Models [53.350633961266375]
  We build a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer, and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.
  arXiv  Detail & Related papers  (2021-08-31T19:39:55Z)
• Transformer-F: A Transformer network with effective methods for learning universal sentence representation [8.225067988604351]
  The Transformer model is widely used in natural language processing for sentence representation. In this paper, two approaches are introduced to improve the performance of Transformers.
  arXiv  Detail & Related papers  (2021-07-02T03:20:11Z)
• Incorporating Convolution Designs into Visual Transformers [24.562955955312187]
  We propose a new textbfConvolution-enhanced image Transformer (CeiT) which combines the advantages of CNNs in extracting low-level features, strengthening locality, and the advantages of Transformers in establishing long-range dependencies. Experimental results on ImageNet and seven downstream tasks show the effectiveness and generalization ability of CeiT compared with previous Transformers and state-of-the-art CNNs, without requiring a large amount of training data and extra CNN teachers.
  arXiv  Detail & Related papers  (2021-03-22T13:16:12Z)
• Multi-branch Attentive Transformer [152.07840447196384]
  We propose a simple yet effective variant of Transformer called multi-branch attentive Transformer. The attention layer is the average of multiple branches and each branch is an independent multi-head attention layer. Experiments on machine translation, code generation and natural language understanding demonstrate that such a simple variant of Transformer brings significant improvements.
  arXiv  Detail & Related papers  (2020-06-18T04:24:28Z)
• Applying the Transformer to Character-level Transduction [68.91664610425114]
  The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. We show that with a large enough batch size, the transformer does indeed outperform recurrent models for character-level tasks.
  arXiv  Detail & Related papers  (2020-05-20T17:25:43Z)
• Segatron: Segment-Aware Transformer for Language Modeling and Understanding [79.84562707201323]
  We propose a segment-aware Transformer (Segatron) to generate better contextual representations from sequential tokens. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset.
  arXiv  Detail & Related papers  (2020-04-30T17:38:27Z)
• Hierarchical Transformer Network for Utterance-level Emotion Recognition [0.0]
  We address some challenges in utter-ance-level emotion recognition (ULER) Unlike the traditional text classification problem, this task is supported by a limited number of datasets. We use a pretrained language model bidirectional encoder representa-tions from transformers (BERT) as the lower-level transformer. In addition, we add speaker embeddings to the model for the first time, which enables our model to capture the in-teraction between speakers.
  arXiv  Detail & Related papers  (2020-02-18T13:44:49Z)

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.