International audience

International audience

Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis About Source code for the Interpseech 2024 paper about automatic classifcation of news subjects in broadcast news. The code contains evaluation scripts, models training and interfence source. Dataset The annotated dataset contains about 03h44min of broadcast news, with 605 dialogues for the Test set. The dataset can be downloaded at the following URL: https://www.ina.fr/recherche/dataset-project, under the name is24_news_topic. Evaluation scripts The scripts to eval your own models are included in the folder evaluation/. You will need an original copy of the dataset (see above.). You may use the following script: evaluation/eval \ --reference ORIGINAL_DATASET_FOLDER \ --prediction PRED_FOLDER/predictions.json \ --subset dev # dev or test The results will be saved inside PRED_FOLDER folder, with the name results.json. You need to format your predictions into the following format: { "DIALOGUE_ID_1": { "text": "input text for that dialogue (this field is not required in your output file, but it allow you to easily browse the file while reading the predicted output, and manually see if what your model predicted)", "class__ARTS/CULTURE/ENTERTAINMENT": false, "class__COMMERCIAL": false, "class__CRIME/LAW/JUSTICE": true, "class__DISASTER/ACCIDENT": false, "class__ECONOMY/BUSINESS/FINANCE": false, "class__EDUCATION": false, "class__ENVIRONMENTAL_ISSUE": false, "class__HEALTH": false, "class__LABOUR": false, "class__LIFESTYLE/LEISURE": false, "class__OTHER": false, "class__POLITICS": false, "class__RELIGION/BELIEF": false, "class__SCIENCE/TECHNOLOGY": false, "class__SOCIAL_ISSUE": false, "class__SPORT": false, "class__UNREST/CONFLICTS/WAR": true, "class__WEATHER": false }, "DIALOGUE_ID_2": { "..." } } Models The source code of training and inference of the models presented in the paper are included in the folder models/ (models/BERT and models/Mixtral). BERT finetuning The script to finetune all models presented in the paper is in models/BERT/train_all. It calls the train script. To generate the predictions of a specific model, use models/BERT/predict. You can then use evaluation/eval to get metric scores of your model. Mixtral The prompt used for the Mixtral model is in the file models/Mixtral/prompt.txt. Use the script models/Mixtral/inference_mixtral to generate the outputs of Mixtral (or an other model) base on this prompt and input dialogues. Then, call parse_safe_json to convert the output of the model into a valid JSON format, which you can then use with generate_prediction_dataset to create a valid training/validation/testing dataset. You then can use evaluation/eval to evaluate Mixtral response if the inference was on the dataset dialogues, or models/BERT/train to finetune BERT on these annotations. Installation You need python3, pip3. pip3 install -r requirements.txt Citation If you use this corpus or the source code of this repository, please cite the following article: @inproceedings{pelloin2024automatic, title = {Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysis}, author = {Valentin Pelloin and Lena Dodson and \'Emile Chapuis and Nicolas Hervé and David Doukhan}, booktitle = {Proc. InterSpeech 2024}, month = 9, year = 2024, address = "Kos Island, Greece", }

We aim at improving spoken language modeling (LM) using very large amount of automatically transcribed speech.We leverage the INA (French National Audiovisual Institute 1 ) collection and obtain 19GB of text after applying ASR on 350,000 hours of diverse TV shows.From this, spoken language models are trained either by fine-tuning an existing LM (FlauBERT 2 ) or through training a LM from scratch.New models (FlauBERT-Oral) are shared with the community and evaluated for 3 downstream tasks: spoken language understanding, classification of TV shows and speech syntactic parsing.Results show that FlauBERT-Oral can be beneficial compared to its initial FlauBERT version demonstrating that, despite its inherent noisy nature, ASR-generated text can be used to build spoken language models.

The automatic stance detection task consists in determining the attitude expressed in a text toward a target (text, claim, or entity). This is a typical intermediate task for the fake news detection or analysis, which is a considerably widespread and a particularly difficult issue to overcome. This work aims at the creation of a human-annotated corpus for the automatic stance detection of tweets written in French. It exploits a corpus of tweets collected during July and August 2018. To the best of our knowledge, this is the first freely available stance annotated tweet corpus in the French language. The four classes broadly adopted by the community were chosen for the annotation: support, deny, query, and comment with the addition of the ignore class. This paper presents the corpus along with the tools used to build it, its construction, an analysis of the inter-rater reliability, as well as the challenges and questions that were raised during the building process.

We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweets manually annotated by three annotators as related or unrelated to a given event. The 243 events were selected both from press articles and from subjects trending on Twitter during the annotation period (July to August 2018). In total, more than 95,000 tweets were annotated as related to one of the selected events. We also provide the titles and URLs of 15,500 news articles automatically detected as related to these events. In addition to this corpus, we detail the results of our event detection experiments on both this dataset and another publicly available dataset of tweets in English. We ran extensive tests with different types of text embeddings and a standard Topic Detection and Tracking algorithm, and detail our evaluation method. We show that tf-idf vectors allow the best performance for this task on both corpora. These results are intended to serve as a baseline for researchers wishing to test their own event detection systems on our corpus.

This paper presents an automatic system able to identify visual named-entities appearing in images and videos, among a list of 25,000 entities, aggregated from Wikipedia lists, and more specific websites. DigInPix is a generic application designed to identify different kinds of entities. In this first attempt, we only focus on logo identification (more generally on legal persons). The identification process mainly relies on an efficient CBIR system, searching in an indexed image database composed of 600,000 weak-labelled images crawled from Google Images. DigInPix proposes a responsive-design html5 interface [1] for testing purposes.

Amalia.js is a new extensible and versatile HTML5 multimedia player that allows you to view any type of metadata synchronized with your video or audio streams. It manages metadata that are localized both temporally and spatially. They can also be hierarchical. Several visualization plugins have already been developped, enabling amalia.js to be deployed in a huge variety of web applications. We believe it can be used in various research areas to quickly visualize analysis results and to share them with the community. Amalia.js is an open-source software under a GPL license. It is available for download at http://ina-foss.github.io/amalia.js.

Who said What, Where and How? How are images, video and stories spreading out? Who produces the information? OTMedia addresses these questions by collecting, enriching and analysing continuously more than 1500 streams of French media from TV Radio, Web, AFP, and Twitter. Two studies on media produced by end users with the OTMedia framework are presented.

State-of-the-art visual search methods allow retrieving efficiently small rigid objects in very large image datasets (e.g. logos, paintings, etc.). User's perception of the classical query-by-window paradigm is however affected by the fact that many submitted queries actually return nothing or only junk results. We demonstrate in this demo that the perception can be radically different if the objects of interest are rather suggested to the user by pre-computing relevant clusters of instances. Impressive results involving very small objects discovered in a web collection of 110K images are demonstrated through a simple interactive GUI.

This paper presents a visual-based media event detection system based on the automatic discovery of the most circulated images across the main news media (news websites, press agencies, TV news and newspapers). Its main originality is to rely on the transmedia contextual information to denoise the raw visual detections and consequently focus on the most salient transmedia events.

We introduce two approaches to improve an existing color segmentation technique based on a Split and Merge quantization process for the study of stained histological images. We propose to modify the merge criterion : first, we include a spatial constraints heuristic; then we suggest the use of supervision and a more elaborated visual features representation. We tested these approaches on a renal biopsies dataset to automatically quantify interstitial fibrosis and show that supervision brings very significant improvements.

The bag-of-visual-words is a popular representation for images that has proven to be quite effective for automatic annotation. In this paper, we extend this representation in order to include weak geometrical information by using visual word pairs. We show on a standard benchmark dataset that this new image representation improves significantly the performances of an automatic annotation system.

To reduce the gap between the VLSI technology capability and the designer productivity, design reuse based on IP (intellectual properties) is commonly used. In terms of arithmetic accuracy, the generated architecture can generally only be configured through the input and output word-lengths. In this paper, a new kind of fixed-point arithmetic IP is presented through the LMS and delayed-LMS examples. The operator and memory word-lengths are optimized under an accuracy constraint defined by the user. To significantly reduce the optimization and design times, the architecture parameter determination is based on analytical approach