8440--8451. Without cross-lingual dataset, (Duan et al., 2019; Shen et al., 2018) pro-pose a zero-shot system based on the teacher-student framework, whose student network still learned from the translated data thus suffers from the inaccuracy of the machine translation system. We observed improvements when increasing the dataset size for cross-lingual training, as shown in Table 3. Model Overview UC2 extends monolingual language encoder of V+L frameworks, such as UNITER [10], to cross-lingual en-coder [11], as shown in Figure 2 (b). However, cross-lingual approaches demonstrate similar accuracy levels reaching ~0.831, ~0.829, ~0.853, ~0.831, and ~0.813 on German, French, Lithuanian, Latvian, and Portuguese languages. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. However, any approach needs to have evaluation data to confirm the results. 3.2. This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. The visual feature is extracted from an image encoder and the language feature is obtained from a general cross-lingual language . We construct a visual grounding dataset for French via crowdsourcing. To address the scarcity of datasets in cross-lingual Synopsis. State-of-the-art algorithms usually employ a strongly supervised, resource-rich approach difficult to use for poorly-resourced languages. The accuracy equal to ~0.842 is achieved with the English dataset with completely monolingual models is considered our top-line. Our dataset contains fine-grained parallel data from 8 languages (7 commonly used programming languages and English), and supports 10 cross-lingual code tasks. To illustrate the utility of our dataset we report experiments with multi-lingual pre-trained models in supervised, zero- and few-shot, and out-of-domain scenarios. Recently, XQA, a large cross-lingual Open-QA dataset, becomes public, called S-Based-Extraction task here. For the out-of-domain setting, we introduce Voxeu-rop, a cross-lingual news dataset.3 In experiments, obtain an end-to-end system. This paper presents BiPaR, a bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support multilingual and cross-lingual reading comprehension. The initial translation resources are borrowed from . MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. Jiangshu Du, Yingtong Dou, Congying Xia, Limeng Cui, Jing Ma, Philip S. Yu. The text content comes from the Croatian 24sata daily news portal. Cross-lingual Wikification is the task of grounding mentions written in non-English documents to entries in the English Wikipedia. This corpus was created from 68 Commoncrawl Snapshots (up until March 2020). We evaluate our . SQuAD. Techniques from MT such as word alignment have also inspired much work in cross-lingual representation learning ( Ruder et al., 2019 ). The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set @InProceedings {clads-emnlp, author = "Laura Perez-Beltrachini and Mirella Lapata", title . 2 RELATED WORK 2.1 Cross-lingual Ad-hoc Retrieval Cross-lingual ad-hoc retrieval has always been considered as the combination of machine translation and monolingual ad-hoc re-trieval. Datasets and Resources Download the whole package including a README file. Submission history From: Laura Perez-Beltrachini [ view email ] 35 PDF View 3 excerpts, references background We create a challenging dataset in 12 . Extensive experiments on the newly created dataset verify the effectiveness of our proposed curriculum self-knowledge distillation method for cross-lingual knowledge grounded conversation. Zero-shot Cross-lingual Classification Just like in any other Transformer-based monolingual model, XLM too, is fine-tuned on the XNLI dataset for obtaining the cross-lingual classification. The CoNLL-2002 and CoNLL-2003 datasets are used for English, Dutch, Spanish and German languages. Dec 11, 2021 1 min read. XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment This corpus was created by mining CCAligned, CCMatrix, and WikiMatrix parallel sentences. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. Cross-lingual word representations are used to initialise unsupervised MT ( Artetxe et al., 2019) and cross-lingual representations have been shown to improve neural MT performance ( Lample & Conneau, 2019 ). The Cross-Lingual Sentiment (CLS) dataset comprises about 800.000 Amazon product reviews in the four languages English, German, French, and Japanese. We analyse the proposed cross-lingual summarisation task with automatic metrics and validate it with a human study. Cross-lingual Open Question Answering is a challenging multilingual NLP task, where given questions are written in a user's preferred language, a system needs to find evidence in large-scale document collections written in many different languages, and return an answer in the user's preferred language, as indicated by their question . Both English and French head-dependent vectors cluster; dependencies of the same label in both English . For people who are working on XTREME, we recommend you to submit results to XGLUE as well, especially for the XGLUE unique tasks, as (1) XGLUE includes tasks (News . Cross-lingual NLP and text mining technology is an important alternative in such situations. With cross-lingual transfer capability, we could leverage the rich resources of a few languages to build NLP services for all the languages in the world. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. 1 Paper Code Furthermore, we nd that DROP dataset is especially suitable for cross-lingual transfer for several reasons. Cross-lingual transfer refers to transfer learning using data and models available for one language for which ample such resources are available (e.g., English) to solve tasks in another, commonly more low-resource, language. Please contact me at [email protected] for any question. Consistent with H1, English is the only group to receive more links than it sends (596 vs. 104). The Cross-Lingual Sentiment (CLS) dataset comprises about 800.000 Amazon product reviews in the four languages English, German, French, and Japanese. Cross-Lingual Adversarial Domain Adaptation (CrossLing) framework that can leverage a large programming dataset to learn features that can improve SMP's build using a much smaller dataset in a different programming language. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification . XGLUE. We construct the training set by extracting query-product pairs from real-world user logs. Unlike domain adaptation, instance selection within the cross lingual adaptation has an additional aim to filter out the noisy instances from a selected dataset. This difference is significant (p < 0.0001) as determined by a t-test in UCINET comparing the in-degree and out-degree of English blogs receiving or sending cross . While most of this research is focused on the English language only, the principle extends to transfer between languages, and recent work in cross-lingual transfer leverages datasets in multiple languages to provide pre-trained models with multilingual embeddings ( Artetxe & Schwenk, 2019; Devlin et al., 2019 ). Or, alternatively, download a specific dataset: The RG-65 dataset in Spanish and Farsi. Our proposed methods outperform current state-of-the-art multilingual multimodal models (e.g., M3P) in zero-shot cross-lingual settings, but the accuracy remains low across the board; a performance drop of around 38 accuracy points in target languages showcases the difficulty of zero-shot cross-lingual transfer for this task. We propose EXAMS -- a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. Cross-lingual querying of financial and business data from multi-lingual sources requires that inherent challenges posed by the diversity of financial concepts and languages used in different jurisdictions be addressed. Shared task: Check the SemEval-2017 task on Multilingual and Cross-lingual Semantic Word Similarity. For more information on the construction of the dataset see (Prettenhofer and Stein, 2010) or the enclosed readme files. There are 707 cross-lingual links in the dataset, which represent 5.6% of all links. Download BibTex In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks. Our framework maintains one globally invariant latent representa-tion across both datasets via an adversarial learning . 2019. Cross-lingual Language Model Pretraining. We collect 3,667 bilingual parallel . Please see paper for more detailed evaluation results. It consists of a training set in English as well as development and test sets in eight other languages. GSW Global+Sliding Window attention mechanism [2]. To tackle the second challenge, we collect a cross-lingual knowledge grounded conversation test dataset to facilitate relevant research in the future. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages. Empirical results on our dataset demonstrate the effectiveness of CrossFake under the cross-lingual setting and it also outperforms several monolingual and cross-lingual fake news . Second, DROP entails a variety of tasks related to EXAMS offers a fine-grained evaluation framework across multiple . We introduce a new 1K multi-lingual MSCOCO2014 caption test dataset (XTD10) in 7 languages that we collected using a crowdsourcing platform. Our framework is a pipeline consisting of two components: (1) Concrete, a claim-oriented cross-lingual retriever that retrieves relevant passages from a multilingual passage collection, and (2) a multilingual reader that determines the veracity of a claim based on the compatibility of the claim and the passages retrieved. It covers topics such as health, lifestyle, and automotive news. MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. In this paper, we propose a general cross-lingual transfer learning framework PLATO for program analysis by using a series of techniques that are general to different downstream tasks. The test dataset contains mainly three parts: 1- Document Collection The document collection in our extended data is taken from the CLEF eHealth IR task 2015. Each document contains HTML markup, CSS and javascript code. . Classification of emotions on the English dataset, which do not differ much in terms of pitch and MFCC features, generated the lowest accuracies at or below 31%.", keywords = "Arabic, Emotion recognition, English, Machine learning, Urdu Figure 1 provides the taxonomy of information retrieval datasets from the domain and the language aspects. Description: XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. WordNews English-Chinese Cross-Lingual Word Sense Disambiguation dataset This dataset allows evaluation of WSD systems on a dataset consisting of sentences from news articles written recently in 2015. Introduction. MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. What is BiPaR? No claims of intellectual property are made on the work of preparation of the corpus. The .dtd file of Senseval-2 Lexical Sample task is provided. The proposed framework achieves competitive performance in cross-lingual classification tasks without relying . Download XNLI-15way (12MB, ZIP) Useful XLU resources For more information on the construction of the dataset see (Prettenhofer and Stein, 2010) or the enclosed readme files. 04/29/22 - In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from mov. CCAligned: A Massive Collection of Cross-lingual Web-Document Pairs. Multi-task Learning for Cross-Lingual Sentiment Analysis 3 Sentiment Dataset in Croatian The Croatian dataset 3 was created us-ing guidelines similar to those of the SentiNews dataset. The documents are split into sentences based on punctuations and deduplication is performed. The idea is to develop a system based on one or more languages for which many resources are available, yet be able to use it in many new languages by connecting their linguistic representations. The model is finetuned in the following ways: Trained on the English set to evaluate cross-lingual. It is collected for the task that the training data is in the source language, and the. In this experiment, we focused on the cross-lingual model with the MLT approach, due . Google Scholar Cross Ref; Alexis Conneau and Guillaume Lample. Yes, there are some problems but in general it is still considered ok as we do not have many better ways to construct very large datasets in multiple languages (and in general, "cross-lingual" implies there are texts about the same topic but not translations). Ethan A. Chi. Analysis of emotion recognition from cross-lingual speech : Arabic, English, and Urdu. First, it is more "sample-efcient", as an average passage entails signicantly more questions than conventional MRC datasets, e.g. To fill the gap, we collect and release the first large-scale cross-lingual product retrieval dataset (CLPR-9M). A classification layer is added on top of XLM and it is trained on the English NLI training dataset. Published in: IEEE Intelligent Systems ( Volume: 35 , Issue: 3 , 01 May-June 2020) Article #: Page(s . Unsupervised Cross-lingual Representation Learning at Scale. These leaderboards are used to track progress in Cross-Lingual ASR Datasets Common Voice Most implemented papers Most implemented Social Latest No code Exploiting Adapters for Cross-lingual Low-resource Speech Recognition jindongwang/transferlearning 18 May 2021 these data for cross-lingual cross-modal pre-training. Experiments on publicly available datasets for cross-lingual sentiment classification show that the presented method performs cross-lingual sentiment quantification with high accuracy. Induced Code-Switching Datasets This paper introduces XLCoST, Cross-Lingual Code SnippeT dataset, a new benchmark dataset for cross-lingual code intelligence. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. XNLI provides additional open parallel data for low-resource languages such as Swahili or Urdu. This task involves the problem of comparing textual clues across languages, which requires developing a notion of similarity between text snippets across languages. These three sources were themselves extracted from web data from Commoncrawl Snapshots and Wikipedia snapshots. Authors: An Yan, Xin Eric Wang, Jiangtao Feng, . In Annual Conference on Neural Information Processing Systems. We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. Download: XNLI 1.0 (17MB, ZIP) XNLI can also be used as a 15way parallel corpus of 10,000 sentences, for building or evaluating Machine Translation systems. 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. In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method . In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). To address this issue, we propose a novel Cross-lingual Hi-erarchical Graph Mapping (HGM) to effectively conduct the alignment between languages in the scene graph en-coding space, which benets from contextual information by gathering semantics across different levels of the scene graph. Such noisy instances (or outliers) within the source translated dataset are usually generated due to the language gap and translation errors. PAWS - X: A Cross-lingual Adversarial Dataset for Paraphrase Identification Yinfei Yang , Yuan Zhang , Chris Tar , Jason Baldridge Abstract Most existing work on adversarial data generation focuses on English. In this paper, we present the first work on cross-lingual visual grounding to expand the task to different languages to study an effective yet efficient way for visual grounding on other languages. Models and Datasets for Cross-Lingual Summarisation Laura Perez-Beltrachini , Mirella Lapata Abstract We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. Data in English, German, Italian, Spanish and Farsi. Without any training data of the target language . We use this as the test set for evaluating zero-shot model performance across languages. PLATO allows Bert-based models to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others. We only provide samples for which we could download the images and extract meaningful features. Title: Cross-Lingual Vision-Language Navigation. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by . The format is in a similar format as Senseval-2 English Lexical Sample task. / Farhad, Moomal; . May 26, 2020. This work presents MLSUM, the first large-scale MultiLingual SUMmarization dataset obtained from online newspapers, which contains 1.5M+ article/summary pairs in five different languages and reports cross-lingual comparative analyses based on state-of-the-art systems. Given the absence of cross-lingual information retrieval datasets with claim-like queries, we train the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a self-supervised algorithm that creates training instances by translating the title of a passage. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written parallelly in two languages. Our framework is a pipeline consisting of two com- ponents: (1) CONCRETE, a claim-oriented cross- lingual retriever that retrieves relevant passages from a multilingual passage collection, and (2) a multilingual reader that determines the veracity of a claim based on the compatibility of the claim and the passages retrieved. this work introduces cross-lingual choice of plausible alternatives (xcopa), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, revealing that current methods based on multilingual pretraining and zero-shot fine-tuning transfer suffer from the curse of multilinguality and fall short of performance in Cross-lingual transfer learning is a type of low-resource learning that trains a model with data in one language, such as English, and tests the model on the same task in different languages. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. Finding Cross-Lingual Syntax in Multilingual BERT. The documents were provided in HTML format. The COVID-19 pandemic poses a significant threat to global public health. XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation. Ontologies can be used to semantically align financial concepts and integrate financial facts with other company information . Then the model is evaluated on 15 XNLI languages. Cross-lingual training: These models train their embeddings on a parallel corpus and optimize a cross-lingual constraint between embeddings of different languages that encourages embeddings of similar words to be close to each other in a shared vector space. 7057--7067. Based on this newly introduced dataset, we study how an agent can be trained on existing English instructions but navigate effectively with another language under a zero-shot learning scenario. Benchmarks Add a Result These leaderboards are used to track progress in Cross-Lingual Transfer Libraries We projected head-dependent pairs from both English (light colors) and French (dark colors) into a syntactic space trained on solely English mBERT representations. CrossFake. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic . XLM(-R) Cross-lingual language models proposed in [8, 9]. Cross-lingual datasets: Then, we propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content as much as possible. In this paper we offer a multilingual parallel SRL corpus - X-SRL - for English (EN), Ger- man (DE), French (FR) and Spanish (ES) that is based on English gold annotations and shares the same labeling scheme across languages.1Our cor- pus has two major advantages compared to existing datasets: rst, since it is a parallel corpus, all sen- The included TSV files havean additional column containing automatic German translations of the original English captions. Welcome to the Cross-lingual-Test-Dataset-XTD10 corpus! Our dataset consists of 14k, 3k, and 3k query phrases with their corresponding . lingual and multi-lingual), and language resources (high- and low-resource). More specifically, a pseudo-supervised PARAFAC2 model has been fitted to the training documents of the cross-lingual classification dataset using the pseudo-label information as supervision for the unlabelled target language documents. A tarball that contains a custom train, valid, test split of Conceptual Captions (CC) dataset. An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. approach often leads to sub-optimal cross-lingual mapping. We conducted further experiments with varying training data percentages on the Arabic-TOD dataset, ranging from 5% (50 examples) to 100% (1000 examples). We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. Please cite this paper if you use our code or data. If you have a question after reading the paper and the readme . To illustrate the utility of our dataset we report experiments on supervised, zero-shot, few-shot, and out-of-domain cross-lingual summarisation. The collection includes around 1.1 million documents crawled from medical websites. XLM-R has achieved the best results to date on four cross-lingual understanding benchmarks, with increases of 4.7 percent average accuracy on the XNLI cross-lingual natural language inference dataset, 8.4 percent average F1 score on the recently introduced MLQA question answering dataset, and 2.1 percent F1 score on NER.
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