The Bacteria Biotope (BB) Task is part of the BioNLP Open Shared Tasks and meets the BioNLP-OST standards of quality, originality and data formats. Manually annotated data is provided for training, development and evaluation of information extraction methods. Tools for the detailed evaluation of system outputs are available. Support in performing linguistic processing are provided in the form of analyses created by various state-of-the art tools on the dataset texts.
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We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new mesh-level holistic co-speech dataset. BEAT2 combines MoShed SMPLX body with FLAME head parameters and further refines the modeling of head, neck, and finger movements, offering a community-standardized, high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer, facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover, EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enh
BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV). The findings are mapped onto standard Unified Medical Language System (UMLS) terminology and they cover a wide spectrum of thoracic entities, contrasting with the much more reduced number of entities annotated in previous datasets. Images are stored in high resolution and entities are localized with anatomical labels in a Medical Imaging Data Structure (MIDS) format. In addition, 23 images were annotated by a team of expert radiologists to include semantic segmentation of radiographic findings. Moreover, extensive information is provided, including the patient’s demographic information, type
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BiToD is a bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling. BiToD contains over 7k multi-domain dialogues (144k utterances) with a large and realistic bilingual knowledge base. It serves as an effective benchmark for evaluating bilingual ToD systems and cross-lingual transfer learning approaches.
Bongard-HOI testifies to which extent your few-shot visual learner can quickly induce the true HOI concept from a handful of images and perform reasoning with it. Further, the learner is also expected to transfer the learned few-shot skills to novel HOI concepts compositionally.
8 PAPERS • 1 BENCHMARK
Brazil Air-Traffic
The Japanese-English business conversation corpus, namely Business Scene Dialogue corpus, was constructed in 3 steps:
CHAOS challenge aims the segmentation of abdominal organs (liver, kidneys and spleen) from CT and MRI data. ONsite section of the CHAOS was held in The IEEE International Symposium on Biomedical Imaging (ISBI) on April 11, 2019, Venice, ITALY. Online submissions are still welcome!
Dataset [46 M] and readme: 42,306 movie plot summaries extracted from Wikipedia + aligned metadata extracted from Freebase, including: Movie box office revenue, genre, release date, runtime, and language Character names and aligned information about the actors who portray them, including gender and estimated age at the time of the movie's release Supplement: Stanford CoreNLP-processed summaries [628 M]. All of the plot summaries from above, run through the Stanford CoreNLP pipeline (tagging, parsing, NER and coref).
CaSiNo is a dataset of 1030 negotiation dialogues in English. To create the dataset, two participates take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations.
We provide manual annotations of 14 semantic keypoints for 100,000 car instances (sedan, suv, bus, and truck) from 53,000 images captured from 18 moving cameras at Multiple intersections in Pittsburgh, PA. Please fill the google form to get a email with the download links:
CoNLL-2000 is a dataset for dividing text into syntactically related non-overlapping groups of words, so-called text chunking.
Complementary Commonsense (Com2Sense) is a dataset for benchmarking commonsense reasoning ability of NLP models. This dataset contains 4k statement true/false sentence pairs. The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples. To facilitate a systematic analysis of commonsense capabilities, the dataset is designed along the dimensions of knowledge domains, reasoning scenarios and numeracy.
A large commercial Ads Dataset includes 480K labeled query-ad pairwise data with structured information of image, title, seller, description, and so on.
Deep Learning Hard (DL-HARD) is an annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) questions extensively annotated with query intent categories, answer types, wikified entities, topic categories, and result type metadata from a leading web search engine.
The DSSE-200 is a complex document layout dataset including various dataset styles. The dataset contains 200 images from pictures, PPT, brochure documents, old newspapers and scanned documents.
DocNLI is a large-scale dataset for document-level NLI. DocNLI is transformed from a broad range of NLP problems and covers multiple genres of text. The premises always stay in the document granularity, whereas the hypotheses vary in length from single sentences to passages with hundreds of words. Additionally, DocNLI has pretty limited artifacts which unfortunately widely exist in some popular sentence-level NLI datasets.
The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Earnings-21, a 39-hour corpus of earnings calls containing entity-dense speech from nine different financial sectors. This corpus is intended to benchmark ASR (Automatic Speech Recognition) systems in the wild with special attention towards named entity recognition.
EmoWOZ is the first large-scale open-source dataset for emotion recognition in task-oriented dialogues. It contains emotion annotations for user utterances in the entire MultiWOZ (10k+ human-human dialogues) and DialMAGE (1k human-machine dialogues collected from our human trial). Overall, there are 83k user utterances annotated. In addition, the emotion annotation scheme is tailored to task-oriented dialogues and considers the valence, the elicitor, and the conduct of the user emotion.
A dataset for fine-grained entity typing of knowledge graph entities built from Freebase. It can be used to evaluate entity representations and also mention-level entity typing.
GUM is an open source multilayer English corpus of richly annotated texts from twelve text types. Annotations include:
Global Voices is a multilingual dataset for evaluating cross-lingual summarization methods. It is extracted from social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages.
Global WHEAT Dataset is the first large-scale dataset for wheat head detection from field optical images. It included a very large range of cultivars from differents continents. Wheat is a staple crop grown all over the world and consequently interest in wheat phenotyping spans the globe. Therefore, it is important that models developed for wheat phenotyping, such as wheat head detection networks, generalize between different growing environments around the world.
The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database consists of 2904 short multi-modal video recordings of both bona-fide and presentation attacks. There are 555 bonafide presentations from 51 participants and the remaining 2349 are presentation attacks. The data is recorded from several channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra).
Housekeep a benchmark to evaluate common sense reasoning in the home for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. The dataset contains where humans typically place objects in tidy and untidy houses constituting 1799 objects, 268 object categories, 585 placements, and 105 rooms.
The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
KnowIT VQA is a video dataset with 24,282 human-generated question-answer pairs about The Big Bang Theory. The dataset combines visual, textual and temporal coherence reasoning together with knowledge-based questions, which need of the experience obtained from the viewing of the series to be answered.
Logic2Text is a large-scale dataset with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which poses great challenges on the model's ability to understand the semantics.
This is a dataset containing audio captions and corresponding audio tags for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool. Each file is annotated by multiple annotators that provided tags and a one-sentence description of the audio content.
MM-COVID is a dataset for fake news detection related to COVID-19. This dataset provides the multilingual fake news and the relevant social context. It contains 3,981 pieces of fake news content and 7,192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages.
The MMVP (Multimodal Visual Patterns) Benchmark focuses on identifying "CLIP-blind pairs" – images that appear similar to the CLIP model despite having clear visual differences. These patterns highlight the challenges these systems face in answering straightforward questions, often leading to incorrect responses and hallucinated explanations.
The MRDA corpus consists of about 75 hours of speech from 75 naturally-occurring meetings among 53 speakers. The tagset used for labeling is a modified version of the SWBD-DAMSL tagset. It is annotated with three types of information: marking of the dialogue act segment boundaries, marking of the dialogue acts and marking of correspondences between dialogue acts.
MathMLben is a benchmark to the evaluate tools for mathematical format conversion (LaTeX ↔ MathML ↔ CAS). It comprises semantically annotated and linked formulae extracted from the NTCIR 11/12 arXiv and Wikipedia task / dataset, NIST Digital Library of Mathematical Functions (DLMF) and annotations using the AnnoMathTeX formula and identifier name recommender system (https://annomathtex.wmflabs.org).
We build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we developed a pipeline to easily collect aerial and street city views with ground-truth camera poses, as well as a series of additional data modalities. Flexible control on environmental factors like light, weather, human and car crowd is also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size 28km^2.
MeshRIR is a dataset of acoustic room impulse responses (RIRs) at finely meshed grid points. Two subdatasets are currently available: one consists of IRs in a 3D cuboidal region from a single source, and the other consists of IRs in a 2D square region from an array of 32 sources. This dataset is suitable for evaluating sound field analysis and synthesis methods.
The MuSe-CAR database is a large, multimodal (video, audio, and text) dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.
NELA-GT-2018 is a dataset for the study of misinformation that consists of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. It includes ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust.
The OPRA Dataset was introduced in Demo2Vec: Reasoning Object Affordances From Online Videos (CVPR'18) for reasoning object affordances from online demonstration videos. It contains 11,505 demonstration clips and 2,512 object images scraped from 6 popular YouTube product review channels along with the corresponding affordance annotations. More details can be found on our https://sites.google.com/view/demo2vec/.
Open-Platypus is a family of fine-tuned and merged Large Language Models (LLMs) that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard.
OpenLane-V2 is the world's first perception and reasoning benchmark for scene structure in autonomous driving. The primary task of the dataset is scene structure perception and reasoning, which requires the model to recognize the dynamic drivable states of lanes in the surrounding environment. The challenge of this dataset includes not only detecting lane centerlines and traffic elements but also recognizing the attribute of traffic elements and topology relationships on detected objects.
OPOSUM is a dataset for the training and evaluation of Opinion Summarization models which contains Amazon reviews from six product domains: Laptop Bags, Bluetooth Headsets, Boots, Keyboards, Televisions, and Vacuums. The six training collections were created by downsampling from the Amazon Product Dataset introduced in McAuley et al. (2015) and contain reviews and their respective ratings.
A new large scale plane geometry problem solving dataset called PGPS9K, labeled both fine-grained diagram annotation and interpretable solution program.
QUASAR-S is a large-scale dataset aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. It consists of 37,362 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow. The posts and comments on the website serve as the background corpus for answering the cloze questions. The answer to each question is restricted to be another software entity, from an output vocabulary of 4874 entities.
NLPContributionGraph was introduced as Task 11 at SemEval 2021 for the first time. The task is defined on a dataset of Natural Language Processing (NLP) scholarly articles with their contributions structured to be integrable within Knowledge Graph infrastructures such as the Open Research Knowledge Graph. The structured contribution annotations are provided as (1) Contribution sentences : a set of sentences about the contribution in the article; (2) Scientific terms and relations: a set of scientific terms and relational cue phrases extracted from the contribution sentences; and (3) Triples: semantic statements that pair scientific terms with a relation, modeled toward subject-predicate-object RDF statements for KG building. The Triples are organized under three (mandatory) or more of twelve total information units (viz., ResearchProblem, Approach, Model, Code, Dataset, ExperimentalSetup, Hyperparameters, Baselines, Results, Tasks, Experiments, and AblationAnalysis).
The SmartLights benchmark from Snipstests the capability of controlling lights in different rooms. It consists of 1660 requests which are split into five partitions for a 5-fold evaluation. A sample command could be: “please change the [bedroom] lights to [red]” or “i’d like the [living room] lights to be at [twelve] percent”
TMED is a clinically-motivated benchmark dataset for computer vision and machine learning from limited labeled data.
This dataset is collected from various global and local news sources. Toponyms are manually annotated in the articles with the corresponding entries from GeoNames. In total, the dataset consists of 118 articles.