Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model

• URL: http://arxiv.org/abs/2307.14785v1
• Date: Thu, 27 Jul 2023 11:28:16 GMT
• Title: Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
• Authors: Pavel P\v{r}ib\'a\v{n} and Ond\v{r}ej Pra\v{z}\'ak
• Abstract summary: This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models.
• Score: 6.85316573653194
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new state-of-the-art results on the Czech ABSA.

Related papers

• Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback [50.84142264245052]
  This work introduces the Align-SLM framework to enhance the semantic understanding of textless Spoken Language Models (SLMs) Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO) We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation.
  arXiv  Detail & Related papers  (2024-11-04T06:07:53Z)
• ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets [106.7760874400261]
  This paper presents ML-SUPERB2.0, which is a new benchmark for evaluating pre-trained SSL and supervised speech models. We find performance improvements over the setup of ML-SUPERB, but performance depends on the downstream model design. Also, we find large performance differences between languages and datasets, suggesting the need for more targeted approaches.
  arXiv  Detail & Related papers  (2024-06-12T21:01:26Z)
• Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources [11.257738983764499]
  Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. We enhance "modern" supervised WSD models exploiting two popular SLRs: WordNet and WordNet Domains. We study the effect of different types of semantic features, investigating their interaction with local contexts encoded by means of mixtures of Word Embeddings or Recurrent Neural Networks.
  arXiv  Detail & Related papers  (2024-02-20T13:47:51Z)
• A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors [9.279391026742658]
  We analyze the effect of model size, training objectives, and model architecture on the models' performance as a feature extractor. We develop a novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and synthetic information in the extracted representations.
  arXiv  Detail & Related papers  (2023-11-27T15:58:28Z)
• Unsupervised Lexical Simplification with Context Augmentation [55.318201742039]
  Given a target word and its context, our method generates substitutes based on the target context and additional contexts sampled from monolingual data. We conduct experiments in English, Portuguese, and Spanish on the TSAR-2022 shared task, and show that our model substantially outperforms other unsupervised systems across all languages.
  arXiv  Detail & Related papers  (2023-11-01T05:48:05Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• Entity-Assisted Language Models for Identifying Check-worthy Sentences [23.792877053142636]
  We propose a new uniform framework for text classification and ranking. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF's 2019 & 2020 CheckThat! Labs.
  arXiv  Detail & Related papers  (2022-11-19T12:03:30Z)
• Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis [67.41078214475341]
  We propose Dynamic Re-weighting BERT (DR-BERT) to learn dynamic aspect-oriented semantics for ABSA. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence. We then fine-tune it by incorporating a lightweight Dynamic Re-weighting Adapter (DRA)
  arXiv  Detail & Related papers  (2022-03-30T14:48:46Z)
• Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization [20.572283625521784]
  We develop a neural network based abstractive multi-document summarization (MDS) model. We process the dependency information into the linguistic-guided attention mechanism. With the help of linguistic signals, sentence-level relations can be correctly captured.
  arXiv  Detail & Related papers  (2021-09-23T08:13:35Z)
• Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning [89.64620296557177]
  We propose to incorporate the syntactic structures of the sentences into the deep learning models for targeted opinion word extraction. We also introduce a novel regularization technique to improve the performance of the deep learning models. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-10-26T07:13:17Z)
• Comparative Study of Language Models on Cross-Domain Data with Model Agnostic Explainability [0.0]
  The study compares the state-of-the-art language models - BERT, ELECTRA and its derivatives which include RoBERTa, ALBERT and DistilBERT. The experimental results establish new state-of-the-art for 2013 rating classification task and Financial Phrasebank sentiment detection task with 69% accuracy and 88.2% accuracy respectively.
  arXiv  Detail & Related papers  (2020-09-09T04:31:44Z)

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.