22 research outputs found
Cross-lingual Incongruences in the Annotation of Coreference
In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them. More specifically, we automatically detect full coreference chains in parallel texts and analyse discrepancies in their annotations. In doing so, we wish to find out whether the discrepancies rather derive from language typological constraints, from the translation or the actual annotation process. The results of our study contribute to the referential analysis of similarities and differences across languages and support evaluation of cross-lingual coreference annotation. They are also useful for cross-lingual coreference resolution systems and contrastive linguistic studies
Analysing concatenation approaches to document-level NMT in two different domains
In this paper, we investigate how different aspects of discourse context affect the performance of recent neural MT systems. We describe two popular datasets covering news and movie subtitles and we provide a thorough analysis of the distribution of various document-level features in their domains. Furthermore, we train a set of context-aware MT models on both datasets and propose a comparative evaluation scheme that contrasts coherent context with artificially scrambled documents and absent context, arguing that the impact of discourse-aware MT models will become visible in this way. Our results show that the models are indeed affected by the manipulation of the test data, providing a different view on document-level translation quality than absolute sentence-level scores.Peer reviewe
Annotating tense, mood and voice for English, French and German
We present the first open-source tool forannotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rules, which are applied on dependency trees and leverage information about lemmas,
morphological properties and POS-tags of the verbs. Our tool has an average accuracy of about 76%. The tense, mood and voice features are useful both as features in computational modeling and for corpuslinguistic research
Cross-lingual Incongruences in the Annotation of Coreference
In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them. More specifically, we automatically detect full coreference chains in parallel texts and analyse discrepancies in their annotations. In doing so, we wish to find out whether the discrepancies rather derive from language typological constraints, from the translation or the actual annotation process. The results of our study contribute to the referential analysis of similarities and differences across languages and support evaluation of cross-lingual coreference annotation. They are also useful for cross-lingual coreference resolution systems and contrastive linguistic studies
Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction
We describe the design, the setup, and the
evaluation results of the DiscoMT 2017
shared task on cross-lingual pronoun prediction.
The task asked participants to
predict a target-language pronoun given a
source-language pronoun in the context of
a sentence. We further provided a lemmatized
target-language human-authored
translation of the source sentence, and
automatic word alignments between the
source sentence words and the targetlanguage
lemmata. The aim of the task
was to predict, for each target-language
pronoun placeholder, the word that should
replace it from a small, closed set of
classes, using any type of information that
can be extracted from the entire document.
We offered four subtasks, each for a
different language pair and translation
direction: English-to-French, Englishto-German,
German-to-English, and
Spanish-to-English. Five teams participated
in the shared task, making
submissions for all language pairs. The
evaluation results show that all participating
teams outperformed two strong
n-gram-based language model-based
baseline systems by a sizable margin