Word sense disambiguation is necessary in translation because different word senses often have different translations. Neural machine translation models learn different senses of words as part of an endto-end translation task, and their capability to perform word sense disambiguation has so far not been quantified. We exploit the fact that neural translation models can score arbitrary translations to design a novel cross-lingual word sense disambiguation task that is tailored towards evaluating neural machine translation models. We present a test set of 7,200 lexical ambiguities for German→English, and 6,700 for German→French, and report baseline results. With 70% of lexical ambiguities correctly disambiguated, we find that word sense disambiguation remains a challenging problem for neural machine translation, especially for rare word senses. To improve word sense disambiguation in neural machine translation, we experiment with two methods to integrate sense embeddings. In a first approach we pass sense embeddings as additional input to the neural machine translation system. For the second experiment, we extract lexical chains based on sense embeddings from the document and integrate this information into the NMT model. While a baseline NMT system disambiguates frequent word senses quite reliably, the annotation with both sense labels and lexical chains improves the neural models' performance on rare word senses.
The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German→English show that our method produces correct translations in up to 88% of the changes, improving the translation in 36%-48% of them over the baseline.
This paper presents a method to improve the translation of polysemous nouns, when a previous occurrence of the noun as the head of a compound noun phrase is available in a text. The occurrences are identified through pattern matching rules, which detect XY compounds followed closely by a potentially coreferent occurrence of Y , such as "Nordwand . . . Wand". Two strategies are proposed to improve the translation of the second occurrence of Y : re-using the cached translation of Y from the XY compound, or post-editing the translation of Y using the head of the translation of XY . Experiments are performed on Chinese-toEnglish and German-to-French statistical machine translation, over the WIT3 and Text+Berg corpora respectively, with 261 XY /Y pairs each. The results suggest that while the overall BLEU scores increase only slightly, the translations of the targeted polysemous nouns are significantly improved.
The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection (Küçük and Can, 2020). While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently, i.e. using the same noun in the target text as well. We train and test classifiers that predict consistent translations based on lexical, syntactic, and semantic features. We first evaluate the accuracy of our classifiers intrinsically, in terms of the accuracy of consistency predictions, over a subset of the UN Corpus. Then, we also evaluate them in combination with phrase-based statistical MT systems for Chinese-to-English and Germanto-English. We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers' output, and also use these methods in combination. This improves over the baseline and closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier.
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