Papers with Code 아카이브

Word Alignment

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
7
보존된 평가 기록
11

Word Alignment is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other.

Source: Neural Network-based Word Alignment through Score Aggregation

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

WMT 2016 News

News translation is a recurring WMT task. The test set is a collection of parallel corpora consisting of about 1500 English sentences translated into 5 languages (Czech, German, Finnish, Romanian, Russian, Turkish) and additional 1500 sentences from each of the 5 languages translated to English. For Romanian a third of the test set were released as a development set instead. For Turkish additional 500 sentence development set was released. The sentences were selected from dozens of news websites and translated by professional translators. The training data consists of parallel corpora to train translation models, monolingual corpora to train language models and development sets for tuning. Some training corpora were identical from WMT 2015 (Europarl, United Nations, French-English 10⁹ corpus, Common Crawl, Russian-English parallel data provided by Yandex, Wikipedia Headlines provided by CMU) and some were update (CzEng v1.6pre, News Commentary v11, monolingual news data). Additionally, the following new corpora were added: Romanian Europarl, SETIMES2 from OPUS for Romanian-English and Turkish-English, Monolingual data sets from CommonCrawl. Source: https://paperswithcode.com/paper/findings-of-the-2016-conference-on-machine/ Image Source: https://www.aclweb.org/anthology/W16-2301.pdf

Bangla Word Analogy

We provide a Mikolov-style word-analogy evaluation set specifically for Bangla, with a sample size of 16678, as well as a translated and curated version of the Mikolov dataset, which contains 10594 samples for cross-lingual research.

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