Korea, Republic of

Recently, some span-based methods have
achieved encouraging performances for joint
aspect-sentiment analysis, which first extract
aspects (aspect extraction) by detecting aspect
boundaries and then classify the span-level
sentiments (sentiment classification). However, most existing approaches either sequentially extract task-specific features, leading to
insufficient feature interactions, or they encode aspect features and sentiment features in
a parallel manner, implying that feature representation in each task is largely independent
of each other except for input sharing. Both
of them ignore the internal correlations between the aspect extraction and sentiment classification. To solve this problem, we novelly
propose a hierarchical interactive network (HIASA) to model two-way interactions between
two tasks appropriately, where the hierarchical
interactions involve two steps: shallow-level
interaction and deep-level interaction. First,
we utilize cross-stitch mechanism to combine
the different task-specific features selectively
as the input to ensure proper two-way interactions. Second, the mutual information technique is applied to mutually constrain learning
between two tasks in the output layer, thus the
aspect input and the sentiment input are capable of encoding features of the other task
via backpropagation. Extensive experiments
on three real-world datasets demonstrate HIASA’s superiority over baselines.

COLING 2022

A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis

aspect-based

sentiment analysis

[![](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!<br><br><br>

To gain access to this event page you are required to register.<br> 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.

poster

Understanding textual content is critical to improving the quality of news recommendation. To achieve this goal, recent studies have proposed to apply pre-trained language models (PLMs) such as BERT for semantic-enhanced news recommendation. Despite their great success in offline evaluation, it is challenging to apply such large PLMs in real-time ranking tasks due to the stringent latency requirements in model updating and inference. To bridge this gap, we propose a plug-and-play pre-trainer, namely PREC, to learn both user and news encoders through multi-task pre-training. Instead of directly leveraging sophisticated PLMs for end-to-end inference, we focus on how to use the cached user and item representations to boost the performance of traditional ID-based models for click-through-rate prediction. This enables efficient online inference as well as compatibility to the widely-used models in industry, which would significantly ease the practical deployment. We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial system.

Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation

An after-visit summary (AVS) is a summary note given to patients after their clinical visit. It recaps what happened during their clinical visit and guides patients' disease self-management. Studies have shown that a majority of patients found after-visit summaries useful. However, many physicians face excessive workloads and do not have time to write clear and informative summaries. In this paper, we study the problem of automatic generation of after-visit summaries and examine whether those summaries can convey the gist of clinical visits. We report our findings on a new clinical dataset that contains a large number of electronic health record (EHR) notes and their associated summaries. Our results suggest that generation of lay language after-visit summaries remains a challenging task. Crucially, we introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture the important details of the clinical notes or when it contains hallucinated facts that are potentially detrimental to the summary quality. Automatic and human evaluation demonstrates the effectiveness of our approach in providing writing feedback and supporting physicians.

Generation of Patient After-Visit Summaries to Support Physicians

Training with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains. Our experiments suggest that standard regularisation strategies, such as weight decay and dropout, are ineffective in the face of noisy labels. We propose a simple noisy label detection method that prevents error propagation from the input layer. The approach is based on the observation that the projection of noisy labels is learned through memorisation at advanced stages of learning, and that the Pearson correlation is sensitive to outliers. Extensive experiments over real-world human-disagreement annotations as well as randomly-corrupted and data-augmented labels, across various tasks and domains, demonstrate that our method is effective, regularising noisy labels and improving generalisation performance.

Noisy Label Regularisation for Textual Regression

Intelligent medical services have attracted great research interests for providing automated medical consultation. However, the lack of corpora becomes a main obstacle to related research, particularly data from real scenarios. In this paper, we construct RealMedDial, a Chinese medical dialogue dataset based on real medical consultation. RealMedDial contains 2,637 medical dialogues and 24,255 utterances obtained from Chinese short-video clips of real medical consultations. We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis. We evaluate the performance of medical response generation, department routing and doctor recommendation on RealMedDial. Results show that RealMedDial are applicable to a wide range of NLP tasks with respect to medical dialogue.

RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips

A VQA model processes images and questions simultaneously with rich semantic information. The attention mechanism can highlight fine-grained features with critical information. However, unattended coarse-grained information is also essential for questions involving global elements. We believe that global coarse-grained information and local fine-grained information can complement each other to provide richer comprehensive information. So we propose a dual capsule attention mask network with mutual learning for VQA. Experimental results show that our method outperforms the baselines in terms of VQA performance and interpretability and achieves new SOTA performance on the VQA-v2 dataset.

Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering

This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train SelF-Eval with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models.

SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morphologically, from Yorùbá which uses bare nouns but marks these features
contextually, ambiguities arise. In this work, we perform fine-grained analysis on how an SMT system compares with two NMT systems (BiLSTM and Transformer) when translating bare nouns in Yorùbá into English. We investigate how and to what extent the systems identify BNs, correctly translate them, and compare with human translation patterns. We also analyze the type of errors each model makes and
provide a linguistic description of these errors. We glean insights for evaluating model performance in low-resource settings. In translating bare nouns, our results show the transformer model outperfoms the SMT and BiLSTM models for 4 categories, the BiLSTM outperforms the SMT model for 3 categories while the SMT
outperforms the NMT models for 1 category.

Linguistically-Motivated Yorùbá-English Machine Translation

Emotion cause analysis (ECA) aims to extract emotion clauses and find the corresponding cause of the emotion. Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. These task-specific methods have a deficiency of universality. And the relations among multiple objectives in one task are not explicitly modeled. Moreover, the relative position information introduced in most existing methods may make the model suffer from dataset bias. To address the first two problems, this paper proposes a universal prompt tuning method to solve different ECA tasks in the unified framework. As for the third problem, this paper designs a directional constraint module and a sequential learning module to ease the bias. Considering the commonalities among different tasks, this paper proposes a cross-task training method to further explore the capability of the model. The experimental results show that our method achieves competitive performance on the ECA datasets.

UECA-Prompt: Universal Prompt for Emotion Cause Analysis

Code-mixing refers to the mixed use of multiple languages. It is prevalent in multilingual societies and is also one of the most challenging natural language processing tasks. We study Bahasa Rojak, a dialect popular in Malaysia that consists of English, Malay, and Chinese. Aiming to establish a model to deal with the code-mixing phenomena of Bahasa Rojak, we use data augmentation to automatically construct the first Bahasa Rojak corpus for pre-training language models, which we name the Bahasa Rojak Crawled Corpus (BRCC). We also develop a new pre-trained model called "Mixed XLM". The model can tag the language of the input token automatically to process code-mixing input. Finally, to test the effectiveness of the Mixed XLM model pre-trained on BRCC for social media scenarios where code-mixing is found frequently, we compile a new Bahasa Rojak sentiment analysis dataset, SentiBahasaRojak, with a Kappa value of 0.77.

BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset

We present a corrected version of a previous
projection of the FactBank data set. Previously
published results on FactBank are no longer
valid. We perform experiments on FactBank
using multiple training paradigms, data smoothing
techniques, and polarity classifiers. We argue
that f-measure is an important alternative
evaluation metric for factuality. We provide
new state-of-the-art results for four corpora including
FactBank. We perform an error analysis
on Factbank combined with two similar
corpora.

Downloads

Next from COLING 2022

Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation

Similar lecture

Towards a contextualised spatial-diachronic history of literature: mapping emotional representations of the city and the country in Polish fiction from 1864 to 1939

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES