Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2103.07665v1
• Date: Sat, 13 Mar 2021 09:30:47 GMT
• Title: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
• Authors: Shaowei Chen, Yu Wang, Jie Liu, Yuelin Wang
• Abstract summary: Aspect sentiment triplet extraction (ASTE) is an emerging task in fine-grained opinion mining. We transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task. We propose a bidirectional MRC (BMRC) framework to address this challenge.
• Score: 8.208671244754317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining. Since ASTE consists of multiple subtasks, including opinion entity extraction, relation detection, and sentiment classification, it is critical and challenging to appropriately capture and utilize the associations among them. In this paper, we transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task and propose a bidirectional MRC (BMRC) framework to address this challenge. Specifically, we devise three types of queries, including non-restrictive extraction queries, restrictive extraction queries and sentiment classification queries, to build the associations among different subtasks. Furthermore, considering that an aspect sentiment triplet can derive from either an aspect or an opinion expression, we design a bidirectional MRC structure. One direction sequentially recognizes aspects, opinion expressions, and sentiments to obtain triplets, while the other direction identifies opinion expressions first, then aspects, and at last sentiments. By making the two directions complement each other, our framework can identify triplets more comprehensively. To verify the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets. The experimental results demonstrate that BMRC achieves state-of-the-art performances.

Related papers

• PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
  This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA) To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
  arXiv  Detail & Related papers  (2024-08-18T13:51:01Z)
• Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer [78.35816158511523]
  We present a single-stage emotion recognition approach, employing a Decoupled Subject-Context Transformer (DSCT) for simultaneous subject localization and emotion classification. We evaluate our single-stage framework on two widely used context-aware emotion recognition datasets, CAER-S and EMOTIC.
  arXiv  Detail & Related papers  (2024-04-26T07:30:32Z)
• Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis [72.9124467710526]
  generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task. We propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios.
  arXiv  Detail & Related papers  (2022-10-12T23:38:57Z)
• Span-level Bidirectional Cross-attention Framework for Aspect Sentiment Triplet Extraction [10.522014946035664]
  Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences. We propose a span-level bidirectional cross-attention framework for ASTE. Our framework significantly outperforms state-of-the-art methods, achieving better performance in predicting triplets with multi-token entities.
  arXiv  Detail & Related papers  (2022-04-27T02:55:43Z)
• PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction [12.921737393688245]
  Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step.
  arXiv  Detail & Related papers  (2021-10-10T13:39:39Z)
• Aspect Sentiment Triplet Extraction Using Reinforcement Learning [14.21689018940387]
  We present a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment. We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment. This takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency.
  arXiv  Detail & Related papers  (2021-08-13T07:38:48Z)
• Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video [128.41392860714635]
  We introduce Weakly-Supervised Snoma-Temporally Detection (WSSTAD) in surveillance video. WSSTAD aims to localize a-temporal tube (i.e. sequence of bounding boxes at consecutive times) that encloses abnormal event. We propose a dual-branch network which takes as input proposals with multi-granularities in both spatial-temporal domains.
  arXiv  Detail & Related papers  (2021-08-09T06:11:14Z)
• A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis [9.587513675287829]
  Aspect based sentiment analysis involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Previous approaches fail to solve all subtasks in a unified end-to-end framework. We construct two machine reading comprehension problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing.
  arXiv  Detail & Related papers  (2021-01-04T07:47:53Z)
• Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification [56.893393134328996]
  Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis subtasks. We propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE.
  arXiv  Detail & Related papers  (2020-11-01T11:00:19Z)
• A Multi-task Learning Framework for Opinion Triplet Extraction [24.983625011760328]
  We present a novel view of ABSA as an opinion triplet extraction task. We propose a multi-task learning framework to jointly extract aspect terms and opinion terms. We evaluate the proposed framework on four SemEval benchmarks for ASBA.
  arXiv  Detail & Related papers  (2020-10-04T08:31:54Z)
• An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis [73.7488524683061]
  We propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. Our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.
  arXiv  Detail & Related papers  (2020-04-04T13:49:54Z)

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.