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Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD task since a sentence may contain multiple aspect categories. Therefore, we propose a multi-label few-shot learning method based on the prototypical network. To alleviate the noise, we design two effective attention mechanisms. The support-set attention aims to extract better prototypes by removing irrelevant aspects. The query-set attention computes multiple prototype-specific representations for each query instance, which are then used to compute accurate distances with the corresponding prototypes. To achieve multi-label inference, we further learn a dynamic threshold per instance by a policy network. Extensive experimental results on three datasets demonstrate that the proposed method significantly outperforms strong baselines.

ACL-IJCNLP 2021

Multi-Label Few-Shot Learning for Aspect Category Detection

aspect category detection

multi-label

few-shot learning

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

poster

Generalization is an important ability that helps to ensure that a machine learning model can perform well on unseen data. In this paper, we study the effect of data bias on model generalization, using Chinese Named Entity Recognition (NER) as a case study. Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that can compromise model generalization ability. Firstly, the test sets of all the five datasets contain a significant proportion of entities that have been seen in the training sets. Such test data would therefore not be able to reflect the true generalization ability of a model. Secondly, all datasets are dominated by a few fat-head entities, i.e., entities appearing with particularly high frequency. As a result, a model might be able to produce high prediction accuracy simply by keyword memorization without leveraging context knowledge. To address these data biases, we first refine each test set by excluding seen entities from it, so as to better evaluate a model's generalization ability. Then, we propose a simple yet effective entity resampling method to make entities within the same category distributed equally, encouraging a model to leverage both name and context knowledge in the training process. Experimental results demonstrate that the proposed entity resampling method significantly improves a model’s ability in detecting unseen entities, especially for company, organization and position categories.

Improving Model Generalization: A Chinese Named Entity Recognition Case Study

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively. Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed event-related semantic structures. The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised ``liberal'' EE, which requires jointly extracting events and discovering event schemata without any annotated data. Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting. The source code and pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.

CLEVE: Contrastive Pre-training for Event Extraction

Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. Specifically, we pre-train StructuralLM with two new designs to make the most of the interactions of cell and layout information: 1) each cell as a semantic unit; 2) classification of cell positions. The pre-trained StructuralLM achieves new state-of-the-art results in different types of downstream tasks, including form understanding (from 78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and document image classification (from 94.43 to 96.08).

StructuralLM: Structural Pre-training for Form Understanding

Aspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the improvement is limited due to the inaccuracy of the dependency parsing results and the informal expressions and complexity of online reviews. To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously. Particularly, to alleviate dependency parsing errors, we design a SynGCN module with rich syntactic knowledge. To capture semantic correlations, we design a SemGCN module with self-attention mechanism. Furthermore, we propose orthogonal and differential regularizers to capture semantic correlations between words precisely by constraining attention scores in the SemGCN module. The orthogonal regularizer encourages the SemGCN to learn semantically correlated words with less overlap for each word. The differential regularizer encourages the SemGCN to learn semantic features that the SynGCN fails to capture. Experimental results on three public datasets show that our DualGCN model outperforms state-of-the-art methods and verify the effectiveness of our model.

Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis

In this paper, we define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues. We say that a question is history-dependent if it requires (parts of) their dialogue history to be interpreted. We argue that some kinds of visual questions define a context upon which a follow-up spatial question relies. We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer. We automatically extract different trigger and zoomer pairs based on the visual property that the questions rely on (e.g. color, number). We manually annotate the automatically extracted trigger and zoomer pairs to verify which zoomers require their trigger. We implement a simple baseline architecture based on a SOTA multimodal encoder. It leaves much room for improvement for answering history-dependent questions

Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History

How pre-trained transformer-based language models perform grounded language acquisition through cross-situational learning (CSL) remains unclear.
In particular, it is still not understood how meaning concepts are captured from complex sentences, along with learning language-based interactions,
could benefit the field of human-robot interactions and help understand how children learn and ground language.
In our current work, we study cross-situational learning to understand the mechanisms enabling children to learn rapidly sequence of words to meaning concepts: the two sequence-based models (i) Echo State Networks (i.e., Reservoir Computing), and (ii) Long-Short Term Memory Networks (LSTM) makes this mapping.
We consider the three different input representations: (i) One-Hot encoding, (ii) BERT fine-tuned on Juven+GOLD corpus, and (iii) Google BERT.
We investigate which of these three representations better predict the stimulated vision as a function of sentences describing the scenes using two models.
Using our approach, we test two datasets: Juven and GoLD, and present how these models generalize only after a few hundred partially described scenes via cross-situational learning.
We find that both One-Hot encoding and BERT fine-tuned representations (for both models) significantly improve the predictions.
Moreover, we argue that these models are able to learn complex relations between the contexts in which a word appears and their corresponding meaning concepts, handling polysemous and synonymous words.
This aspect could be incorporated into a human-robot interaction study that examines grounding language to objects in a physical world and poses a challenge for researchers to investigate better the use of transformer models in robotics and HCI.

Learning to Parse Sentences with Cross-Situational Learning using Different Word Embeddings Towards Robot Grounding

Transformer, a neural sequence model entirely based on attention, has achieved great success in natural language processing and become the de facto default model for multiple NLP tasks. Albeit its prevalence, the attention-based structure poses unmet challenges that the widely-used adaptive optimization methods, e.g., Adam, have serious difficulty in learning and often fail to converge if applied alone. In this work, we illustrate the problem that the adaptive optimization methods produce extremely-large learning rates that break the balance of stability. We further propose AdaMod, which smooths out extremely-large learning rates with adaptive and momental upper bounds on a per-parameter basis, instead of the uniform scaling in the warmup scheme. We empirically demonstrate AdaMod can improve the learning stability and bring significant improvements to the performance of Transformers and CNNs. Moreover, empirical results verify its effectiveness and robustness across different applications.

An Adaptive Learning Method for Solving the Extreme Learning Rate Problem of Transformer

Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks.

Investigation on Data Adaptation Techniques for Neural Named Entity Recognition

Non-autoregressive neural machine translation (NAT) usually employs sequence-level knowledge distillation using autoregressive neural machine translation (AT) as its teacher model.
However, a NAT model often outputs shorter sentences than an AT model.
In this work, we propose sequence-level knowledge distillation (SKD) using perturbed length-aware positional encoding and apply it to a student model, the Levenshtein Transformer.
Our method outperformed a standard Levenshtein Transformer by 2.5 points in bilingual evaluation understudy (BLEU) at maximum in a WMT14 German to English translation.
The NAT model output longer sentences than the baseline NAT models.

Multi-Label Few-Shot Learning for Aspect Category Detection

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