1,898 research outputs found
Identifying Semantic Divergences in Parallel Text without Annotations
Recognizing that even correct translations are not always semantically
equivalent, we automatically detect meaning divergences in parallel sentence
pairs with a deep neural model of bilingual semantic similarity which can be
trained for any parallel corpus without any manual annotation. We show that our
semantic model detects divergences more accurately than models based on surface
features derived from word alignments, and that these divergences matter for
neural machine translation.Comment: Accepted as a full paper to NAACL 201
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data
In recent years, the field of document understanding has progressed a lot. A
significant part of this progress has been possible thanks to the use of
language models pretrained on large amounts of documents. However, pretraining
corpora used in the domain of document understanding are single domain,
monolingual, or nonpublic. Our goal in this paper is to propose an efficient
pipeline for creating a big-scale, diverse, multilingual corpus of PDF files
from all over the Internet using Common Crawl, as PDF files are the most
canonical types of documents as considered in document understanding. We
analysed extensively all of the steps of the pipeline and proposed a solution
which is a trade-off between data quality and processing time. We also share a
CCpdf corpus in a form or an index of PDF files along with a script for
downloading them, which produces a collection useful for language model
pretraining. The dataset and tools published with this paper offer researchers
the opportunity to develop even better multilingual language models.Comment: Accepted at ICDAR 202
Producing Monolingual and ParallelWeb Corpora at the Same Time – SpiderLing and Bitextor’s Love Affair
This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering linguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain .hr and the Slovene top-level domain .si, and extrinsically on the English–Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English–Croatian, English–Finnish, English–Serbian and English–Slovene language pairs.This research is supported by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (AbuMaTran)
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