The Winograd Schema Challenge was introduced both as an alternative to the Turing Test and as a test of a system’s ability to do commonsense reasoning. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes.
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OntoNotes 5.0 is a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference).
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The CoNLL dataset is a widely used resource in the field of natural language processing (NLP). The term “CoNLL” stands for Conference on Natural Language Learning. It originates from a series of shared tasks organized at the Conferences of Natural Language Learning.
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WinoBias contains 3,160 sentences, split equally for development and test, created by researchers familiar with the project. Sentences were created to follow two prototypical templates but annotators were encouraged to come up with scenarios where entities could be interacting in plausible ways. Templates were selected to be challenging and designed to cover cases requiring semantics and syntax separately.
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GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications.
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The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and Arabic, using the final version, v5.0, of the OntoNotes corpus. It was a follow-on to the English-only task organized in 2011.
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The ECB+ corpus is an extension to the EventCorefBank (ECB, Bejan and Harabagiu, 2010). A newly added corpus component consists of 502 documents that belong to the 43 topics of the ECB but that describe different seminal events than those already captured in the ECB. All corpus texts were found through Google Search and were annotated with mentions of events and their times, locations, human and non-human participants as well as with within- and cross-document event and entity coreference information. The 2012 version of annotation of the ECB corpus (Lee et al., 2012) was used as a starting point for re-annotation of the ECB according to the ECB+ annotation guideline.
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GAP is a graph processing benchmark suite with the goal of helping to standardize graph processing evaluations. Fewer differences between graph processing evaluations will make it easier to compare different research efforts and quantify improvements. The benchmark not only specifies graph kernels, input graphs, and evaluation methodologies, but it also provides optimized baseline implementations. These baseline implementations are representative of state-of-the-art performance, and thus new contributions should outperform them to demonstrate an improvement. The input graphs are sized appropriately for shared memory platforms, but any implementation on any platform that conforms to the benchmark's specifications could be compared. This benchmark suite can be used in a variety of settings. Graph framework developers can demonstrate the generality of their programming model by implementing all of the benchmark's kernels and delivering competitive performance on all of the benchmark's gra
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Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
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English Web Treebank is a dataset containing 254,830 word-level tokens and 16,624 sentence-level tokens of webtext in 1174 files annotated for sentence- and word-level tokenization, part-of-speech, and syntactic structure. The data is roughly evenly divided across five genres: weblogs, newsgroups, email, reviews, and question-answers. The files were manually annotated following the sentence-level tokenization guidelines for web text and the word-level tokenization guidelines developed for English treebanks in the DARPA GALE project. Only text from the subject line and message body of posts, articles, messages and question-answers were collected and annotated.
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xP3 is a multilingual dataset for multitask prompted finetuning. It is a composite of supervised datasets in 46 languages with English and machine-translated prompts.
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WikiCoref is an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia.
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LitBank is an annotated dataset of 100 works of English-language fiction to support tasks in natural language processing and the computational humanities, described in more detail in the following publications:
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A large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering.
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The 'Deutsche Welle corpus for Information Extraction' (DWIE) is a multi-task dataset that combines four main Information Extraction (IE) annotation sub-tasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document.
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Maybe Ambiguous Pronoun is a dataset similar to GAP dataset, but without binary gender constraints.
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ParCorFull is a parallel corpus annotated with full coreference chains that has been created to address an important problem that machine translation and other multilingual natural language processing (NLP) technologies face -- translation of coreference across languages. This corpus contains parallel texts for the language pair English-German, two major European languages. Despite being typologically very close, these languages still have systemic differences in the realisation of coreference, and thus pose problems for multilingual coreference resolution and machine translation. This parallel corpus covers the genres of planned speech (public lectures) and newswire. It is richly annotated for coreference in both languages, including annotation of both nominal coreference and reference to antecedents expressed as clauses, sentences and verb phrases.
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CLEVR-Dialog is a large diagnostic dataset for studying multi-round reasoning in visual dialog. Specifically, that authors construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset. This combination results in a dataset where all aspects of the visual dialog are fully annotated. In total, CLEVR-Dialog contains 5 instances of 10-round dialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs.
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A large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.
GUM is an open source multilayer English corpus of richly annotated texts from twelve text types. Annotations include:
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Consists of multiple sentences whose clues are arranged by difficulty (from obscure to obvious) and uniquely identify a well-known entity such as those found on Wikipedia.
Composes sentence pairs (i.e., twin sentences).
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OntoGUM is an OntoNotes-like coreference dataset converted from GUM, an English corpus covering 12 genres using deterministic rules.
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Winogender Schemas is a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender.
VisPro dataset contains coreference annotation of 29,722 pronouns from 5,000 dialogues.
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An unsupervised dataset for co-reference resolution. Presented in the publication: Kocijan et. al, WikiCREM: A Large Unsupervised Corpus for Coreference Resolution, presented at EMNLP 2019.
AMALGUM is a machine annotated multilayer corpus following the same design and annotation layers as GUM, but substantially larger (around 4M tokens). The goal of this corpus is to close the gap between high quality, richly annotated, but small datasets, and the larger but shallowly annotated corpora that are often scraped from the Web.
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GICoref is a fully annotated coreference resolution dataset written by and about trans people.
BiPaR is a manually annotated bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support monolingual, multilingual and cross-lingual reading comprehension on novels. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written in parallel in two languages. BiPaR is diverse in prefixes of questions, answer types and relationships between questions and passages. Answering the questions requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality.
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A large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English.
XWINO is a multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities.
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A Game Of Sorts is a collaborative image ranking task. Players are asked to rank a set of images based on a given sorting criterion. The game provides a framework for the evaluation of visually grounded language understanding and generation of referring expressions in multimodal dialogue settings.
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MultiReQA is a cross-domain evaluation for retrieval question answering models. Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus. MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the MRQA shared task. MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data.
A dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. The dataset bridges the gap between coreference resolution and summarization.
Comet is a dataset which contains 11.5k user-assistant dialogs (totalling 103k utterances), grounded in simulated personal memory graphs.
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CoreSearch is a dataset for Cross-Document Event Coreference Search. It consists of two separate passage collections: (1) a collection of passages containing manually annotated coreferring event mention, and (2) an annotated collection of destructor passages.
The DocRED Information Extraction (DocRED-IE) dataset extends the DocRED dataset for the Document-level Closed Information Extraction (DocIE) task. DocRED-IE is a multi-task dataset and allows for 5 subtasks: (i) Document-level Relation Extraction, (ii) Mention Detection, (iii) Entity Typing, (iv) Entity Disambiguation, (v) Coreference Resolution, as well as combinations thereof such as Named Entity Recognition (NER) or Entity Linking. The DocRED-IE dataset also allows for the end-to-end tasks of: (i) DocIE and (ii) Joint Entity and Relation Extraction. DocRED-IE comprises sentence-level and document-level facts, thereby describing short as well as long-range interactions within an entire document.
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The Manually Annotated Sub-Corpus (MASC) consists of approximately 500,000 words of contemporary American English written and spoken data drawn from the Open American National Corpus (OANC).
Describe the Marmara Turkish Coreference Corpus, which is an annotation of the whole METU-Sabanci Turkish Treebank with mentions and coreference chains.
<Task description: joint learning of coreference resolution and query rewrite>
SciCo is an expert-annotated dataset for hierarchical CDCR (cross-document coreference resolution) for concepts in scientific papers, with the goal of jointly inferring coreference clusters and hierarchy between them.
This dataset consists of Winograd schemas that test coreference resolution systems' ability to differentiate singular vs plural they/them pronouns. It consists of 4077 templates, each with a group of people, a singular person (which can be filled with a name or a generic "someone") and a single they/them pronoun to resolve.
Current approaches to context-aware MT rely on a set of surface heuristics to translate pronouns, which break down when translations require real reasoning. We create a new template test set ContraCAT to assess the ability of Machine Translation to handle the specific steps necessary for successful pronoun translation.
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