Korea, Republic of

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts are also difficult for human annotators. In fact, we can obtain some potentially helpful information from correlated texts, which have some common entities, to help the text understanding. Then, one can easily reason out the correct answer by referencing correlated samples. In this paper, we suggest enhancing NER models with correlated samples. We draw correlated samples by the sparse BM25 retriever from large-scale in-domain unlabeled data. To explicitly simulate the human reasoning process, we perform a training-free entity type calibrating by majority voting. To capture correlation features in the training stage, we suggest to model correlated samples by the transformer-based multi-instance cross-encoder. Empirical results on datasets of the above two domains show the efficacy of our methods.

COLING 2022

Domain-Specific NER via Retrieving Correlated Samples

chinese ner

retrieval

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Generating synthetic data for supervised learning from large-scale pre-trained language models has enhanced performances across several NLP tasks, especially in low-resource scenarios. In particular, many studies of data augmentation employ masked language models to replace words with other words in a sentence. However, most of them are evaluated on sentence classification tasks and cannot immediately be applied to tasks related to the sentence structure. In this paper, we propose a simple yet effective approach to generating sentences with a coordinate structure in which the boundaries of its conjuncts are explicitly specified. For a given span in a sentence, our method embeds a mask with a coordinating conjunction in two ways (“X and <mask>”, “<mask> and X”) and forces masked language models to fill the two blanks with an identical text. To achieve this, we introduce decoding methods for BERT and T5 models with the constraint that predictions for different masks are synchronized. Furthermore, we develop a training framework that effectively selects synthetic examples for the supervised coordination disambiguation task. We demonstrate that our method produces promising coordination instances that provide gains for the task in low-resource settings.

Coordination Generation via Synchronized Text-Infilling

This paper presents a language-independent approach for morphological
disambiguation which has been regarded as an extension of POS tagging, jointly
predicting complex morphological tags. In the proposed approach, all words, roots,
POS and morpheme tags are embedded into vectors, and context representations from
surface word and morphological contexts are calculated. Then the inner products
between analyses and the context’s representations are computed to perform the
disambiguation. The underlying hypothesis is that the correct morphological analysis
should be closer to the context in a vector space. Experimental results show that the
proposed approach out-performs the existing models on seven different language
datasets. Concretely, compared with the baselines of MarMot and a sophisticated
neural model (Seq2Seq), the proposed approach achieves around 6% improvement in
average accuracy for all languages while running about 6 and 33 times faster than
MarMot and Seq2Seq, respectively.

Language-Independent Approach for Morphological Disambiguation

Multi-label Text Classification (MLTC) is the task of categorizing documents into one or more topics. Considering the large volumes of data and varying domains of such tasks, fully-supervised learning requires manually fully annotated datasets which is costly and time-consuming. In this paper, we propose BERT-Flow-VAE (BFV), a Weakly-Supervised Multi-Label Text Classification (WSMLTC) model that reduces the need for full supervision. This new model: (1) produces BERT sentence embeddings and calibrates them using a flow model, (2) generates an initial topic-document matrix by averaging results of a seeded sparse topic model and a textual entailment model that only require surface name of topics and 4-6 seed words per topic, and (3) adopts a VAE framework to reconstruct the embeddings under the guidance of the topic-document matrix. Finally, (4) it uses the means produced by the encoder model in the VAE architecture as predictions for MLTC. Experimental results on 6 multi-label datasets show that BFV can substantially outperform other baseline WSMLTC models in key metrics and achieve approximately 84% performance of a fully-supervised model.

BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification

Essay exams have been attracting attention as a way of measuring the higher-order abilities of examinees, but they have two major drawbacks in that grading them is expensive and raises questions about fairness. As an approach to overcome these problems, automated essay scoring (AES) is in increasing need. Many AES models based on deep neural networks have been proposed in recent years and have achieved high accuracy, but most of these models are designed to predict only a single overall score. However, to provide detailed feedback in practical situations, we often require not only the overall score but also analytic scores corresponding to various aspects of the essay. Several neural AES models that can predict both the analytic scores and the overall score have also been proposed for this very purpose. However, conventional models are designed to have complex neural architectures for each analytic score, which makes interpreting the score prediction difficult. To improve the interpretability of the prediction while maintaining scoring accuracy, we propose a new neural model for automated analytic scoring that integrates a multidimensional item response theory model, which is a popular psychometric model.

Analytic Automated Essay Scoring based on Deep Neural Networks Integrating Multidimensional Item Response Theory

In this presentation, we would help interested viewers to delve into how and why structural bias is necessary for aspect sentiment triplet extraction.

Structural Bias for Aspect Sentiment Triplet Extraction

In our presentation, we introduce our paper "Position Offset Label Prediction for Grammatical Error Correction". We mainly talk about our analysis on grammatical error correction task and corpus. We introduce our model which is designed according to the characteristic of this task. We report experimental result on GEC dataset of different languages.

Position Offset Label Prediction for Grammatical Error Correction

Chinese Grammatical Error Diagnosis (CGED) suffers the problems of numerous types of grammatical errors and insufficiency of training data. In this paper, we propose a string editing based CGED model that requires less training data by using a unified workflow to handle various types of grammatical errors. Two measures
are proposed in our model to enhance the performance of CGED. First, the detection and correction of grammatical errors are divided into different stages. In the stage of error detection, the model only outputs the types of grammatical errors so that the tag vocabulary size is significantly reduced compared with other string editing based models. Secondly, the correction of some grammatical errors is
converted to the task of masked character inference, which has plenty of training data and mature solutions. Experiments on datasets of NLPTEA-CGED demonstrate that our model outperforms other CGED models in many aspects.

String Editing based Chinese Grammatical Error Diagnosis

Aspect-based sentiment analysis (ABSA) has received increasing attention recently. ABSA can be divided into multiple tasks according to the different extracted elements. Existing generative methods usually treat the output as a whole string rather than the combination of different elements and only focus on a single task at once. This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts. Further, the proposed approach can train on simple tasks and transfer to difficult tasks by assembling task prompts, like assembling Lego bricks. We conduct experiments on six ABSA tasks across multiple benchmarks. Our proposed multi-task approach achieves new state-of-the-art results in almost all tasks and competitive results in task transfer scenarios.

LEGO-ABSA: A prompt-based task assemblable unified generative framework for multi-task Aspect-based sentiment analysis

The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models with respect to a probabilistic structured loss, where the forward pass of the language models captures semantics and the loss reconstructs structures. Our extensive experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method. We also show that our method can significantly improve the performance in a low-resource regime, thanks to the better use of semantics. The code and datasets are available at https://github.com/pkusjh/LASS.

Joint Language Semantic and Structure Embedding for Knowledge Graph Completion

Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate. To this end, we propose a method to jointly model semantic and
syntactic information from medical texts based on causal explanation theory. We generate dependency forests consisting of the semanticembedded 1-best dependency tree. Then, a taskspecific causal explainer is adopted to prune the dependency forests, which are further fed into a designed graph convolutional network to learn the corresponding representation for downstream task. Empirically, the various comparisons on benchmark medical datasets demonstrate the effectiveness of our model.

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Domain-Specific NER via Retrieving Correlated Samples

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Coordination Generation via Synchronized Text-Infilling

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