Korea, Republic of

We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition. We first fine-tune LUKE, a state-of-the-art Named Entity Recognition (NER) model, on a limited amount of transcripts, then use it as the teacher model to teach a smaller DistilBERT-based student model using a large amount of weakly labeled data and a small amount of human-annotated data. The model achieves high accuracy while also satisfying the practical constraints for inclusion in a commercial telephony product: realtime performance when deployed on cost-effective CPUs rather than GPUs.

COLING 2022

An Effective, Performant Named Entity Recognition System for
Noisy Business Telephone Conversation Transcripts

luke

distillation

named entity recognition

knowledge distillation

## 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

workshop paper

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### 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.

Scientific medical terms are difficult to understand for laypeople due to their technical formulas and etymology. Understanding medical concepts is important for laypeople as personal and public health is a lifelong concern. In this study, we present our methodology for building a French lexical resource annotated with paraphrases for the simplification of monolexical and multiword medical terms. In order to find medical paraphrases, we automatically searched for medical terms and specific lexical markers that help to paraphrase them. We annotated the medical terms, the paraphrase markers, and the paraphrase. We analysed the lexical relations and semantico-pragmatic functions that exists between the term and its paraphrase. We computed statistics for the medical paraphrase corpus, and we evaluated the readability of the medical paraphrases for a non-specialist coder. Our results show that medical paraphrases from popularization texts are easier to understand (62.66%) than paraphrases extracted from scientific texts (50%).

Identifying Medical Paraphrases in Scientific versus Popularization Texts in French for Laypeople Understanding

Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. We also report and discuss results from experiments applying common baseline extractive text summarization algorithms to this dataset. Qualitative analysis of the results reveal shortcomings in common annotation approaches and evaluation methods when applied to narrative and interactive narrative datasets. The dataset is released as open source for future researchers to train and test their own approaches for IDN text.

IDN-Sum : A New Dataset for Interactive Digital Narrative Extractive Text Summarisation

The present paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or
even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multi-layer network is constructed by establishing linkages between the different parts. During training phases, several hyper-parameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest.


Summarization of Long Input Texts Using Multi-Layer Neural Network

We present the results of the Workshop on Automatic Summarization for Creative Writing 2022 Shared Task on summarization of chapters from novels. In this task, we finetune a pretrained transformer model for long documents called LongformerEncoderDecoder which supports seq2seq tasks for long inputs which can be up to 16k tokens in length. We use the Booksum dataset for longform narrative summarization for training and validation, which maps chapters from novels, plays and stories to highly abstractive human written summaries. We use a summary of summaries approach to generate the final summaries for the blind test set, in which we recursively divide the text into paragraphs, summarize them, concatenate all resultant summaries and repeat this process until either a specified summary length is reached or there is no significant change in summary length in consecutive iterations. Our best model achieves a ROUGE-1 F-1 score of 29.75, a ROUGE-2 F-1 score of 7.89 and a BERT F- 1 score of 54.10 on the shared task blind test dataset.

LED Finteuning and Recursive Summary Generation for Automatic Summarization of Chapters from Novels

Challenges and solutions for summarization of book chapters and dialogues

Where did I read that? Challenges in Summarization of Book Chapters and Dialogues

The Creative Summarization Shared Task at COLING 2022 aspires to generate summaries given long-form texts from creative writing. This paper presents the system architecture and the results of our participation in the Scriptbase track that focuses on generating movie plots given movie scripts. The core innovation in our model employs a two-stage hierarchical architecture for movie script summarization. In the first stage, a heuristic extraction method is applied to extract actions and essential dialogues, which reduces the average length of input movie scripts by 66\% from about 24K to 8K tokens. In the second stage, a state-of-the-art encoder-decoder model, Longformer-Encoder-Decoder (LED), is trained with effective fine-tuning methods, \emph{BitFit} and \emph{NoisyTune}. Evaluations on the unseen test set indicate that our system outperforms both zero-shot LED baselines as well as other participants on various automatic metrics and ranks 1st in the Scriptbase track.

Two-Stage Movie Scripts Summarization: An Efficient Method For Low-resource Long Documents Summarization

Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts. In previous work, noise is often introduced for all words (which is likely overestimating the impact) or single untargeted words (likely underestimating the vulnerability). We perform a user study asking people to select words they would obfuscate in a post. Using this realistic setting, we find that the real vulnerability of hate speech detection systems against deliberately introduced noise is almost as high as when using a white-box attack and much more severe than when using a non-targeted dictionary. Our results are based on 4 different datasets, 12 different obfuscation strategies, and hate speech detection systems using different paradigms.

Analyzing the Real Vulnerability of Hate Speech Detection Systems against Targeted Intentional Noise

Monitoring vaccine behaviour through social media can guide health policy. We present a new dataset of 9471 tweets posted in Australia from 2020 to 2022, annotated with sentiment toward vaccines and also 5C, the five types
of behaviour toward vaccines, a scheme commonly used in health psychology literature. We benchmark our dataset using BERT and Gradient Boosting Machine and show that jointly training both sentiment and 5C tasks (F1=48) outperforms individual training (F1=39) in this highly imbalanced data. Our sentiment analysis
indicates close correlation between the sentiments and prominent events during the pandemic. We hope that our dataset and benchmark models will inform further work in online monitoring of vaccine behaviour. The dataset and benchmark methods are accessible online.

Automatic Identification of 5C Vaccine Behaviour on Social Media

The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively. This paper measures the potential of prediction models on that task, based on a real-world dataset of Dutch Twitter conversations in the domain of customer service. We find that modeling emotion trajectories has a small, but measurable benefit compared to predictions based on isolated turns. The models used in our study are shown to generalize well to different companies and economic sectors.

An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues

In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated. This phenomenon has been significantly amplified by the fast and inexorable spread of misinformation on social media and instant messaging platforms. With this work, we contribute to containing the negative impact on society caused by fake news. We propose a graph neural network approach for detecting false information on Twitter. We leverage the inherent structure of graph-based social media data aggregating information from short text messages (tweets), user profiles and social interactions. We use knowledge from pre-trained language models efficiently, and show that user-defined descriptions of profiles provide useful information for improved prediction performance. The empirical results indicate that our proposed framework significantly outperforms text- and user-based methods on misinformation datasets from two different domains, even in a difficult multilingual setting.

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An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts

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Identifying Medical Paraphrases in Scientific versus Popularization Texts in French for Laypeople Understanding

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