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Incorporating attribute information such as user and product features into deep neural networks has been proven to be useful in sentiment analysis. Previous works typically accomplished this in two manners: concatenate multiple attributes to word/text representation or treat them as a bias to adjust attention distribution. To leverage the advantages of both methods, this paper proposes a multi-attribute BERT (MA-BERT) to incorporate external attribute knowledge. The proposed method has two advantages. First, it applies multi-attribute transformer (MA-Transformer) encoders to incorporate multiple attributes into both input representation and attention distribution. Second, the MA-Transformer is implemented as a universal layer and stacked on a BERT-based model such that it can be initialized from a pre-trained checkpoint and fine-tuned for the downstream applications without extra costs on pre-training. Experiments on three benchmark datasets show that the proposed method outperformed pre-trained BERT models and other methods incorporating external attribute knowledge.

IJCNLP-AACL 2021

MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers

learning representation

attention mechanism

sentiment analysis

**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).

technical paper

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks.
In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner.
Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. 
Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction

In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named \texttt{CNERTA}. Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data. Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features. Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task. Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER. (2) Our proposed model yields state-of-the-art (SoTA) results on \texttt{CNERTA}, demonstrating its effectiveness. For further research, the annotated dataset is publicly available at \url{http://github.com/DianboWork/CNERTA}.

A Large-Scale Chinese Multimodal NER Dataset with Speech Clues

Disease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for many downstream applications. Previous studies have demonstrated that joint modeling of the two sub-tasks has superior performance than the pipelined counterpart. Although the neural joint model based on multi-task learning framework has achieved state-of-the-art performance, it suffers from the boundary inconsistency problem due to the separate decoding procedures. Moreover, it ignores the rich information (e.g., the text surface form) of each candidate concept in the vocabulary, which is quite essential for entity normalization. In this work, we propose a neural transition-based joint model to alleviate these two issues. We transform the end-to-end disease recognition and normalization task as an action sequence prediction task, which not only jointly learns the model with shared representations of the input, but also jointly searches the output by state transitions in one search space. Moreover, we introduce attention mechanisms to take advantage of the text surface form of each candidate concept for better normalization performance. Experimental results conducted on two publicly available datasets show the effectiveness of the proposed method.

A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization

Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. 
Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. 
To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. 
We enrich event ontology with linkages among event types, and further induce more event-event correlations. 
Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. 
Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. 
Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

OntoED: Low-resource Event Detection with Ontology Embedding

Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based on a randomly sampled subset of large-scale monolingual data, which we empirically show is sub-optimal. In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data. To this end, we compute the uncertainty of monolingual sentences using the bilingual dictionary extracted from the parallel data. Intuitively, monolingual sentences with lower uncertainty generally correspond to easy-to-translate patterns which may not provide additional gains. Accordingly, we design an uncertainty-based sampling strategy to efficiently exploit the monolingual data for self-training, in which monolingual sentences with higher uncertainty would be sampled with higher probability. Experimental results on large-scale WMT English⇒German and English⇒Chinese datasets demonstrate the effectiveness of the proposed approach. Extensive analyses suggest that emphasizing the learning on uncertain monolingual sentences by our approach does improve the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.

Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation

Question generation is the task of generating coherent and relevant questions given context paragraphs. Recently, with the development of large-scale question answering datasets such as SQuAD, the English question generation has been rapidly developed. However, for other languages such as Chinese, the available training data is limited, which hinders the development of question generation in the corresponding language. To investigate the multi-lingual question generation, in this paper, we develop a language-agnostic language model, which learns the shared representation from several languages in a single architecture. We propose an adversarial training objective to encourage the model to learn both language-specific and language-independent information. We utilize abundant monolingual text to improve the multi-lingual question generation via pre-training. With the language-agnostic language model, we achieve significant improvement in multi-lingual question generation over five languages. In addition, we propose a large-scale Chinese question generation dataset containing more than 220k human-generated questions to benefit the multi-lingual question generation research.

Multi-Lingual Question Generation with Language Agnostic Language Model

Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. However, recent research has suggested that phrase-level representations in these models reflect heavy influences 
of lexical content, but lack evidence of sophisticated, compositional phrase information. Here we investigate the impact of fine-tuning on the capacity of contextualized embeddings to capture phrase meaning information beyond lexical content. Specifically, we fine-tune models on an adversarial paraphrase classification task with high lexical overlap, and on a sentiment classification task. After fine-tuning, we analyze phrasal representations in controlled settings following prior work. We find that fine-tuning largely fails to benefit compositionality in these representations, though training on sentiment yields a small, localized benefit for certain models. In follow-up analyses, we identify confounding cues in the paraphrase dataset that may explain the lack of composition benefits from that task, and we discuss factors underlying the localized benefits from sentiment training.

On the Interplay Between Fine-tuning and Composition in Transformers

Lifelong topic models mainly focus on in-domain text streams in which each chunk only contains documents from a single domain. To overcome data diversity of the in-domain corpus, most of the existing methods exploit the information from limited sources in a separate and heuristic manner. In this study, we develop a lifelong collaborative model (LCM) based on non-negative matrix factorization to accurately learn topics and domain-specific word embeddings. LCM particularly investigates: (1) developing a knowledge graph based on the semantic relationships among words in the lifelong learning process, so as to accumulate global context information discovered by topic models and local context information reflected by context word embeddings from previous domains, and (2) developing a subword graph based on byte pair encoding and pairwise word relationships to exploit subword information of words in the current in-domain corpus. To the best of our knowledge, we are the first to collaboratively learn topics and word embeddings via lifelong learning. Experiments on real-world in-domain text streams validate the effectiveness of our method.

Lifelong Learning of Topics and Domain-Specific Word Embeddings

Interactive argument pair identification is essential in the context of dialogical argumentation mining. Existing research treats it as a problem of sentence matching and largely relies on textual information to compute the similarities. However, the interaction of opinions usually involves the background of the topic and requires reasoning of knowledge, which is beyond textual information. In this paper, we propose to leverage external knowledge to enhance the identification of interactive argument pairs. We construct the argumentation knowledge graph from the discussion thread of the target topic in the online forum. The interaction between the original argument and the reply is then represented as the path of concepts in the knowledge graph. In practice, we utilize Graph Convolutional Network (GCN) to learn the concept representation in the knowledge graph and use a Transformer-based encoder to learn the representation of paths. Finally, an information alignment network is employed to capture the interaction of textual information of conceptual information (both concept-level and path-level). Experiment results indicate that our model achieves state-of-the-art performance in the benchmark dataset. Further analysis demonstrates the effectiveness of our model for enforcing knowledge reasoning through paths in the knowledge graph.

MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers

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