Classifying discourse relations is known as a hard task, relying on complex indices. On the other hand, discourse-annotated data is scarce, especially for languages other than English: many corpora, of limited size, exist for several languages but the domain is split between different theoretical frameworks that have a huge impact on the nature of the textual spans to be linked, and the label set used. Moreover, each annotation project implements modifications compared to the theoretical background and other projects. These discrepancies hinder the development of systems taking advantage of all the available data to tackle data sparsity and work on transfer between languages is very limited, almost nonexistent between frameworks, while it could improve our understanding of some theoretical aspects and enhance many applications. In this paper, we propose the first experiments on zero-shot learning for discourse relation classification and investigate several paths in the way source data can be combined, either based on languages, frameworks, or similarity measures. We demonstrate how difficult transfer is for the task at hand, and that the most impactful factor is label set divergence, where the notion of underlying framework possibly conceals crucial disagreements.

Zero-shot learning for multilingual discourse relation classification

Discourse relation classification within a multilingual, cross-framework setting is a challenging task, 
and the best-performing systems so far have relied on monolingual and mono-framework approaches.
In this paper, we introduce transformer-based multilingual models, trained jointly over all datasets---thus covering different languages and discourse frameworks. We demonstrate their ability to outperform single-corpus models and to overcome (to some extent) the disparity among corpora, by relying on linguistic features and generic information about the nature of the datasets. We also compare the performance of different multilingual pretrained models, as well as the encoding of the relation direction, a key component for the task. Our results on the 16 datasets of the DISRPT 2021 benchmark show improvements in accuracy in (almost) all datasets compared to the monolingual models, with at best $65.91$\% in average accuracy, thus corresponding to a 4% improvement over the state-of-the-art.

Feature-augmented model for multilingual discourse relation classification

Question Generation (QG), the process of generating meaningful questions from a given context, has proven to be useful for several tasks such as question answering or FAQ generation. While most existing QG techniques generate simple, fact-based questions, this research aims to generate questions that can have complex answers (e.g. "why" questions). We propose a data augmentation method that uses discourse relations to create such questions, and experiment on existing English data. Our approach generates questions based solely on the context without answer supervision, in order to enhance question diversity and complexity. We use an encoder-decoder trained on the augmented dataset to generate either one question or multiple questions at a time, and show that the latter improves over the baseline model when doing a human quality evaluation, without degrading performance according to standard automated metrics.

Complex question generation using discourse-based data augmentation

This paper describes our approach to Subtask 1 "News Genre Categorization" of SemEval-2023 Task 3 "Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", which aims to determine whether a given news article is an opinion piece, an objective report, or satirical. We fine-tuned the domain-specific language model POLITICS, which was pre-trained on a large-scale dataset of more than 3.6M English political news articles following ideology-driven pre-training objectives. In order to use it in the multilingual setup of the task, we added as a pre-processing step the translation of all documents into English. Our system ranked among the top systems overall in most language, and ranked 1st on the English dataset.

MELODI at SemEval-2023 Task 3: In-domain Pre-training for Low-resource Classification of News Articles

Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks. While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features foreign to written text. Segmentation is less studied for this type of language, where annotated data is scarce, and existing corpora more heterogeneous. We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts. Supervision is given by a latent model bootstrapped by manually defined heuristic rules that use linguistic and acoustic information. The resulting model improves the original segmenter, especially in contexts where information on speaker turns is lacking or noisy, gaining up to 13% in F-score. Evaluation is performed on data like those used to define our heuristic rules, but also on transcripts from two other corpora.

Weakly supervised discourse segmentation for multiparty oral conversations

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The last ten years have seen a dramatic improvement in the ability of NLP systems to understand and produce words and sentences. This development has created a renewed interest in discourse phenomena as researchers move towards the processing of long-form text and conversations. There is a surge of activity in discourse parsing, coherence models, text summarization, corpora for discourse level reading comprehension, and discourse related/aided representation learning, to name a few, but the problems in computational approaches to discourse are still substantial. At this juncture, we have organized four Workshops on Computational Approaches to Discourse (CODI) at EMNLP 2020, EMNLP 2021, COLING 2022 and ACL 2023 to bring together discourse experts and upcoming researchers. These workshops have catalyzed work to improve the speed and knowledge needed to solve such problems and have served as a forum for the discussion of suitable datasets and reliable evaluation methods. [**Read more**](https://sites.google.com/view/codi2024/). [**Invited Speakers**](https://sites.google.com/view/codi2024/invited-speakers) **Program Overview** 
09:15 - 09:30 Opening Remarks 
09:30 - 10:30 Invited Talk - Hannah Rohde: Inferences of additional coherence-driven meaning within and across clauses 
10:30 - 11:00 Coffee Break 
11:00 - 12:00 Poster Session 1 
12:00 - 13:30 Lunch 
13:30 - 14:30 Invited Talk - Manfred Stede: Connectives and Arguments 
14:30 - 15:30 Poster Session 2 
15:30 - 16:00 Coffee Break 
16:00 - 17:30 Session - Oral presentations 
17:30 - 17:45 Closing Remarks [**Detailed Program**](https://sites.google.com/view/codi2024/schedule)

W1: 5th Workshop on Computational Approaches to Discourse (CODI)

workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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Please register!

EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

To view Day 2 Live Session Recording click [**here**](https://underline.io/events/395/sessions?eventSessionId=15300&searchGroup=lecture) SemEval is a series of international natural language processing (NLP) research workshops whose mission is to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a range of increasingly challenging problems in natural language semantics. Each year's workshop features a collection of shared tasks in which computational semantic analysis systems designed by different teams are presented and compared.

SemEval is sponsored by the SIGLEX Special Interest Group on the Lexicon of the Association for Computational Linguistics. For more information on SemEval and its history, please see http://en.wikipedia.org/wiki/SemEval.
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W1: The 17th International Workshop on Semantic Evaluation (SemEval)_Day 1

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

**Session Chair: **Kristen Johnson 
**In Person Volunteer:** Ananya B Sai 
** Remote Volunteer:** Anastasiia Sedova

3E: Discourse and Pragmatics

technical paper

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Philippe Muller

6

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SHORT BIO

Presentations

Zero-shot learning for multilingual discourse relation classification

Feature-augmented model for multilingual discourse relation classification

Complex question generation using discourse-based data augmentation

MELODI at SemEval-2023 Task 3: In-domain Pre-training for Low-resource Classification of News Articles

Weakly supervised discourse segmentation for multiparty oral conversations

Weakly supervised discourse segmentation for multiparty oral conversations

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