Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. Each quintuple comprises five elements: subject, object, aspect, opinion and preference. With the rise of Large Language Models (LLMs), existing work primarily focuses on enhancing the performance of COQE task through data augmentation, supervised fine-tuning and instruction tuning. Instead of the above pre-modeling and in-modeling design techniques, we focus on innovation in the post-processing. We introduce a model-unaware adaptive chain-of-feedback (COF) method from the perspective of inference feedback and extraction revision. This method comprises three core modules: dynamic example selection, self-critique and self-revision. By integrating LLMs, COF enables dynamic iterative self-optimization, making it applicable across different baselines. To validate the effectiveness of our approach, we utilize the outputs of two distinct baselines as inputs for COF: frozen parameters few-shot learning and the SOTA supervised fine-tuned model. We evaluate our approach on three benchmarks: Camera, Car and Ele. Experimental results show that, compared to the few-shot learning method, our approach achieves F1 score improvements of 3.51%, 2.65% and 5.28% for exact matching on the respective dataset. Even more impressively, our method further boosts performance, surpassing the current SOTA results, with additional gains of 0.76%, 6.54%, and 2.36% across the three datasets.

To mitigate the need for large-scale labeled data and reduce the computational costs for adapting the model to new tasks, this study explores in-context learning (ICL) for aspect term extraction (ATE) with large language models (LLMs). We propose the dynamic sentence type prompting tailored for aspect term extraction tasks. Our hypothesis suggests that LLMs can learn task-specific aspects from demonstrations in ICL.We build upon the exploration of ICL for ATE and introduce a method for constructing explicit and sentence-type based demonstrations. This method shifts from arbitrary example selection to a systematic approach which highlights critical sentence types. We are inspired by human analogical reasoning and introduce the link-of-analogy prompting. This enables LLMs to generalize effectively to new ATE tasks. LLMs can draw analogies to known aspects which improves their performance on unseen categories. Meanwhile, the large language models can refine the extraction performance by iteratively refining the extracted results based on the specified criterion. We conduct experiments using the SemEval benchmark corpora, including R14-16 and L14, focusing on the domains of Restaurant and Laptop. Experiments demonstrate that our approach outperforms existing prompting and few-shot learning methods on token-level ATE datasets.

Event Detection (ED) is a task of automatically extracting multi-class trigger words. The understanding of word sense is crucial for ED. In this paper, we utilize context-specific commonsense knowledge to strengthen word sense modeling. Specifically, we leverage a Context-specific Knowledge Selector (CKS) to select the exact commonsense knowledge of words from a large knowledge base, i.e., ConceptNet. Context-specific selection is made in terms of the relevance of knowledge to the living contexts. On this basis, we incorporate the commonsense knowledge into the word-level representations before decoding. ChatGPT is an ideal generative CKS when the prompts are deliberately designed, though it is cost-prohibitive. To avoid the heavy reliance on ChatGPT, we train an offline CKS using the predictions of ChatGPT over a small number of examples (about 9% of all). We experiment on the benchmark ACE-2005 dataset. The test results show that our approach yields substantial improvements compared to the BERT baseline, achieving the F1-score of about 78.3%. All models, source codes and data will be made publicly available.

Aspect term extraction (ATE) is an important Natural Language Processing task, which aims to extract aspect terms from reviews. Recently, data augmentation has emerged as a reliable approach for relieving data sparsity in the NLP area. For ATE, self-labeling and semi-generation methods have been proposed to implement effective data augmentation. However, they either rely on external data or a pretrained generation model. In this paper, we propose a simple and self-contained augmentation method, which produces new instances for augmentation by context decoupling and infrequent term refilling, without using external data and generation models. We conduct experiments on four benchmark SemEval datasets. The test results show that our method yields substantial improvements, and performs comparably to the state-of-the-art method which uses external data.

We tackle Aspect Term Extraction (ATE), a task that automatically recognizes aspect terms conditioned on the under-standing of word-level semantics. Due to the capacity of enriching linguistic phenomena for learning, data augmentation contributes to the establishment of robust ATE models. In this paper, we propose to leverage back translation to augment the training data for ATE. It is grounded on the potential advantages that the back-translated instances generally appear as paraphrases, providing diverse pragmatic modes for learning when semantics remains unchanged. This helps to enhance ATE models in recognizing aspect terms when varied contexts and morphologically-different words occur during test. In our experiments, we apply an off-the-shelf Neural Machine Translation (NMT) model for back translation, using French, Chinese and German as interlanguages, respectively. Besides, word alignment is conducted to designate aspect terms in the back-translated cases. Experimental results on SemEval benchmarks show that retraining with the augmented data produces substantial improvements, reaching up to 3.46% at best. In addition, the experiments suggest that 1) family languages are more beneficial than non-family for the aforementioned data augmentation, and 2) selective sampling produces positive effects in the low-resource settings, It is noteworthy that back translation has been explored for data augmentation in other fields, with the aim to enhance neural language modeling. Nevertheless, it hasn't yet been systematically studied towards the ATE task. Although a vest-pocket method is provided in this paper, the comprehensive analysis is conducted, including that on interlanguage selection, low-resource application, as well as compatibility with both conventional and pretrained neural models, besides that in the common comparison and ablation experiments. All the models and codes in the experiments will be made publicly available to support reproducible research.

Comparative Opinion Quintuple Extraction (COQE) is a task of recognizing comparative relationships in a sentence-level comment. It is additionally required to extract the quintuple constituents that attribute to a specific comparative relation, including a subject and object, as well as comparative aspect, opinion and preference. Previous study employ pipeline models in addressing the COQE task, which tend to suffer from error propagation. To address the issue, in this paper, we propose a BERT-based end-to-end neural model as the alternative. Specifically, we first boil COQE down to a set prediction problem, due to the finding that all the quintuple constituents fail to hold coherent or sequential relationships. In other word, we consider a group of quintuple components as a set, and intent to bag-and-drag them as a whole, instead individually and one-by-one. Furthermore, we leverage Graph Convolutional Network (GCN) to enhance the end-to-end model, which plays the role of perceiving and representing the relevant relations among the quintuple components. We experiment on three benchmark datasets, including Camera-COQE, Car-COQE and Ele-COQE. The experimental results show that our model (GCN-E2E) yields a significant improvement in most cases, compared to the BERT-based pipeline baseline. The performance reaches the F1-scores of 14.10%, 36.46% and 39.29%, with the improvements of 0.74%, 6.71% and 8.56%.

We tackle Aspect Term Extraction (ATE), an important natural language processing task that automatically identifies words of domain-specific aspects. Recently, a variety of sophisticated neural models and learning strategies have been explored for enhancing ATE, and significant improvements have been obtained. We intend to strengthen the neural models by involving features of Part-Of-Speech (POS) and syntactic dependency into the encoding process. It is motivated by the empirical findings that features of linguistic structure help to refine the understanding of semantics. Accordingly, we propose a stepwise encoding approach, where POS and syntactic dependency are successively leveraged step by step, including 1) joint encoding over both word and POS sequences using a pretrained language model; 2) BiGRU-based representational refinement conditioned on semantics-aware POS information and POS-aware semantic information; 3) representational augmentation by convolutional encoding of dependency graph. We conduct experiments on the four benchmark datasets of Semantic Evaluation (SemEval) for ATE. Experimental results show that our method obtains substantial improvements on all the considered datasets, and the performance ($F1$-score) reaches 87.69%, 89.76%, 77.94% and 83.96% for L-14, R-14, R-15 and R-16, respectively. All the models and source codes in the experiments will be made publicly available to support reproducible research.

Aspect Term Extraction (ATE) is a natural language processing task, which identifies the languages describing product attributes. Such languages (words) are referred to aspect terms in this field. The current neural ATE models suffer from sparsity of available training data. As a result, they fail to be robust in real applications due to overfitting. More seriously, the distinguishable underlying features cannot be learned sufficiently by neural networks, which causes high misjudgement rates. Deliberate data expansion by human annotation undoubtedly helps to alleviate the problem. However, it is time-consuming. In order to overcome the bottleneck, we utilize the Regularized Dropout (R-Drop) approach to implement self data augmentation, creating variant distributed representations for learning in the real-time computation process of neural networks. More importantly, we propose to conduct contrastive learning over the self-augmented data, which sufficiently leverages the variant distributed representations to explore the distinguishable features. We experiment on four widely-used benchmark datasets (R14-16 and L14) in the shared tasks of Semantic Evaluation (SemEval). Experimental results show that contrastive learning over self-augmented data yields significant performance gains, where the improvement is up to 1.9% F1-score at best. In addition, it is demonstrated in the experiments that our method outperforms the state of the art on two test sets (R14 and R16), and meanwhile achieves competitive performance on the rest test sets.

The following topics are dealt with: formal verification; formal specification; program testing; automata theory; programming language semantics; object-oriented programming; program debugging; theorem proving; temporal logic; multi-agent systems.