| Challenge: | Existing studies on ABSA use a sequence tagging problem to extract aspect-specific opinion words from the sentence given the aspect. |
| Approach: | They build a series of simple yet insightful neural baselines to deal with E2E-ABSA task using contextualized embeddings from pre-trained language models. |
| Outcome: | The proposed architecture outperforms state-of-the-art models even with a simple linear classification layer. |
Similar Papers
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies show impressive results on aspects-based sentiment analysis tasks. |
| Approach: | They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks. |
| Outcome: | The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks. |
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (N19-1)
Copied to clipboard
| Challenge: | Sentiment analysis (SA) is a computational task that aims to identify opinion polarity towards a specific aspect. |
| Approach: | They propose to convert ABSA into a sentence-pair classification task such as question answering and natural language inference. |
| Outcome: | The proposed model is fine-tuned and achieves state-of-the-art on SentiHood and SemEval-2014 datasets. |
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences. |
| Approach: | They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs. |
| Outcome: | The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models. |
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence. |
| Approach: | They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA. |
| Outcome: | The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets. |
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
| Approach: | They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor. |
| Outcome: | The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks. |
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning. |
| Approach: | They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds. |
| Outcome: | The proposed task solves the problems in an aspect-based sentiment analysis task. |
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification (2020.lrec-1)
Copied to clipboard
| Challenge: | Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect Based Sentimence Analysis (ABSA) . recent deep transfer-learning methods have been applied successfully to a myriad of NLP tasks. |
| Approach: | They propose to use a self-supervised domain-specific BERT language model to exploit ATSC . they also perform cross-domain evaluation to explore the real-world robustness of their models . |
| Outcome: | The proposed model outperforms baseline models on the SemEval 2014 task 4 restaurants dataset. |
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task. |
| Approach: | They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features . |
| Outcome: | The proposed method improves on the state-of-the-art in two aspects of the ABSA task. |
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself. |
| Approach: | They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces. |
| Outcome: | The proposed version outperforms baseline models on unsupervised question answering, supervised relation classification and entity linking tasks. |
Contextual Embeddings: When Are They Worth It? (2020.acl-main)
Copied to clipboard
| Challenge: | In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference. |
| Approach: | They empirically compare contextual embeddings with classic pretrained embedders and a random word embeddable with a simple baseline. |
| Outcome: | The proposed models perform within 5 to 10% accuracy on industry-scale data. |