Sentiment analysis with adaptive multi-head attention in Transformer

• URL: http://arxiv.org/abs/2310.14505v4
• Date: Mon, 11 Mar 2024 04:13:50 GMT
• Title: Sentiment analysis with adaptive multi-head attention in Transformer
• Abstract summary: We propose a novel framework based on the attention mechanism to identify the sentiment of a movie review document. In this paper, we propose an adaptive multi-head attention architecture (AdaptAttn) which varies the number of attention heads based on length of sentences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel framework based on the attention mechanism to identify the sentiment of a movie review document. Previous efforts on deep neural networks with attention mechanisms focus on encoder and decoder with fixed numbers of multi-head attention. Therefore, we need a mechanism to stop the attention process automatically if no more useful information can be read from the memory.In this paper, we propose an adaptive multi-head attention architecture (AdaptAttn) which varies the number of attention heads based on length of sentences. AdaptAttn has a data preprocessing step where each document is classified into any one of the three bins small, medium or large based on length of the sentence. The document classified as small goes through two heads in each layer, the medium group passes four heads and the large group is processed by eight heads. We examine the merit of our model on the Stanford large movie review dataset. The experimental results show that the F1 score from our model is on par with the baseline model.

Related papers

• Understanding In-context Learning of Addition via Activation Subspaces [74.8874431046062]
  We study a family of few-shot learning tasks for which the true prediction rule is to add an integer $k$ to the input.<n>We find that Llama-3-8B attains high accuracy on this task for a range of $k$, and localizes its few-shot ability to just three attention heads.
  arXiv  Detail & Related papers  (2025-05-08T11:32:46Z)
• Hawk: An Industrial-strength Multi-label Document Classifier [0.0]
  The paper describes the significance of these problems in detail and proposes a unique neural network architecture that addresses the above problems. A hydranet-like architecture is designed to have granular control over and improve the modularity, coupled with a weighted loss driving task-specific heads. The experimental results reveal that the proposed model outperforms the existing methods by a substantial margin.
  arXiv  Detail & Related papers  (2023-01-15T09:52:18Z)
• Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension [13.191437539419681]
  We propose a novel approach called Adaptive Bidirectional Attention-Capsule Network (ABA-Net) ABA-Net adaptively exploits the source representations of different levels to the predictor. We set the new state-of-the-art performance on the SQuAD 1.0 dataset.
  arXiv  Detail & Related papers  (2022-08-18T10:14:32Z)
• Compositional Attention: Disentangling Search and Retrieval [66.7108739597771]
  Multi-head, key-value attention is the backbone of the Transformer model and its variants. Standard attention heads learn a rigid mapping between search and retrieval. We propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure.
  arXiv  Detail & Related papers  (2021-10-18T15:47:38Z)
• PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization [16.830963601598242]
  We propose PRIMER, a pre-trained model for multi-document representation with focus on summarization. Specifically, we adopt the Longformer architecture with proper input transformation and global attention to fit for multi-document inputs. Our model, PRIMER, outperforms current state-of-the-art models on most of these settings with large margins.
  arXiv  Detail & Related papers  (2021-10-16T07:22:24Z)
• Paperswithtopic: Topic Identification from Paper Title Only [5.025654873456756]
  We present a dataset of papers paired by title and sub-field from the field of artificial intelligence (AI) We also present results on how to predict a paper's AI sub-field from a given paper title only. For the transformer models, we also present gradient-based, attention visualizations to further explain the model's classification process.
  arXiv  Detail & Related papers  (2021-10-09T06:32:09Z)
• Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation [81.55533657694016]
  We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three) We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
  arXiv  Detail & Related papers  (2020-11-02T08:07:50Z)
• Learning Hard Retrieval Decoder Attention for Transformers [69.40942736249397]
  Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. We show that our hard retrieval attention mechanism is 1.43 times faster in decoding.
  arXiv  Detail & Related papers  (2020-09-30T13:18:57Z)
• Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA [96.10612095576333]
  We propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Our model is also comprised of dual-level attention (word/object and frame level), multi-head self-cross-integration for different sources (video and dense captions), and which pass more relevant information to gates. We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2020-05-13T16:35:27Z)
• Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation [73.11214377092121]
  We propose to replace all but one attention head of each encoder layer with simple fixed -- non-learnable -- attentive patterns. Our experiments with different data sizes and multiple language pairs show that fixing the attention heads on the encoder side of the Transformer at training time does not impact the translation quality.
  arXiv  Detail & Related papers  (2020-02-24T13:53:06Z)
• Low-Rank Bottleneck in Multi-head Attention Models [74.83235382203604]
  We argue that the scaling between the number of heads and the size of each head in the current architecture gives rise to a low-rank bottleneck in attention heads. We propose to set the head size of an attention unit to input sequence length, and independent of the number of heads, resulting in multi-head attention layers with provably more expressive power.
  arXiv  Detail & Related papers  (2020-02-17T16:16:40Z)

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.