In this paper, we present the process we used in order to collect new annotations of opinions over the multimodal corpus SEMAINE composed of dyadic interactions. The dataset had already been annotated continuously in two affective dimensions related to the emotions: Valence and Arousal. We annotated the part of SEMAINE called Solid SAL composed of 79 interactions between a user and an operator playing the role of a virtual agent designed to engage a person in a sustained, emotionally colored conversation. We aligned the audio at the word level using the available high-quality manual transcriptions. The annotated dataset contains 5627 speech turns for a total of 73,944 words, corresponding to 6 hours 20 minutes of dyadic interactions. Each interaction has been labeled by three annotators at the speech turn level following a three-step process. This method allows us to obtain a precise annotation regarding the opinion of a speaker. We obtain thus a dataset dense in opinions, with more than 48% of the annotated speech turns containing at least one opinion. We then propose a new baseline for the detection of opinions in interactions improving slightly a state of the art model with RoBERTa embeddings. The obtained results on the database are promising with a F1-score at 0.72.

We present a new dataset of online debates in English, annotated with stance. The dataset was scraped from the “Debating Europe” platform, where users exchange opinions over different subjects related to the European Union. The dataset is composed of 2600 comments pertaining to 18 debates related to the “European Green Deal”, in a conversational setting. After presenting the dataset and the annotated sub-part, we pre-train a model for a multilingual stance classification over the X-stance dataset before fine-tuning it over our dataset, and vice-versa. The fine-tuned models are shown to improve stance classification performance on each of the datasets, even though they have different languages, topics and targets. Subsequently, we propose to enhance the performances over “Debating Europe” with an interaction-aware model, taking advantage of the online debate structure of the platform. We also propose a semi-supervised self-training method to take advantage of the imbalanced and unlabeled data from the whole website, leading to a final improvement of accuracy by 3.4% over a Vanilla XLM-R model.

Stance Recognition over proposals is the task of automatically detecting whether a comment on a specific proposal is in favor of this proposal, against this proposal or that neither inference is likely. The dataset that we propose to use is an online debating platform inaugurated in 2021, where users can submit proposals and comment over proposals or over other comments. It contains 4.2k proposals and 20k comments focused on various topics. Every comment and proposal can come written in another language, with more than 40% of the proposal/comment pairs containing at least two languages, creating a unique intra-multilingual setting. A portion of the data (more than 7k comment/proposal pairs, in 26 languages) was annotated by the writers with a self-tag assessing whether they are in favor or against the proposal. Another part of the data (without self-tag) has been manually annotated: 1206 comments in 6 morphologically different languages (fr, de, en, el, it, hu) were tagged, leading to a Krippendorff’s α of 0.69. This setting allows defining an intra-multilingual and multi-target stance classification task over online debates.

This paper presents the results that were obtained from WASSA 2022 shared task on predicting empathy, emotion, and personality in reaction to news stories. Participants were given access to a dataset comprising empathic reactions to news stories where harm is done to a person, group, or other. These reactions consist of essays and Batson’s empathic concern and personal distress scores. The dataset was further extended in WASSA 2021 shared task to include news articles, person-level demographic information (e.g. age, gender), personality information, and Ekman’s six basic emotions at essay level Participation was encouraged in four tracks: predicting empathy and distress scores, predicting emotion categories, predicting personality and predicting interpersonal reactivity. In total, 14 teams participated in the shared task. We summarize the methods and resources used by the participating teams.

This paper presents the results that were obtained from the WASSA 2021 shared task on predicting empathy and emotions. The participants were given access to a dataset comprising empathic reactions to news stories where harm is done to a person, group, or other. These reactions consist of essays, Batson empathic concern, and personal distress scores, and the dataset was further extended with news articles, person-level demographic information (age, gender, ethnicity, income, education level), and personality information. Additionally, emotion labels, namely Ekman’s six basic emotions, were added to the essays at both the document and sentence level. Participation was encouraged in two tracks: predicting empathy and predicting emotion categories. In total five teams participated in the shared task. We summarize the methods and resources used by the participating teams.

Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.

In this paper we present a new method to learn a model robust to typos for a Named Entity Recognition task. Our improvement over existing methods helps the model to take into account the context of the sentence inside a justice decision in order to recognize an entity with a typo. We used state-of-the-art models and enriched the last layer of the neural network with high-level information linked with the potential of the word to be a certain type of entity. More precisely, we utilized the similarities between the word and the potential entity candidates the tagged sentence context. The experiments on a dataset of french justice decisions show a reduction of the relative F1-score error of 32%, upgrading the score obtained with the most competitive fine-tuned state-of-the-art system from 94.85% to 96.52%.