• Grammatical annotation of historical Portuguese: Generating a corpus-based diachronic dictionary

      Bick, Eckhard; Zampieri, Marcos (Springer, 2016-09-03)
      In this paper, we present an automatic system for the morphosyntactic annotation and lexicographical evaluation of historical Portuguese corpora. Using rule-based orthographical normalization, we were able to apply a standard parser (PALAVRAS) to historical data (Colonia corpus) and to achieve accurate annotation for both POS and syntax. By aligning original and standardized word forms, our method allows to create tailor-made standardization dictionaries for historical Portuguese with optional period or author frequencies.
    • Improving translation memory matching and retrieval using paraphrases

      Gupta, Rohit; Orasan, Constantin; Zampieri, Marcos; Vela, Mihaela; van Genabith, Josef; Mitkov, Ruslan (Springer Nature, 2016-11-02)
      Most of the current Translation Memory (TM) systems work on string level (character or word level) and lack semantic knowledge while matching. They use simple edit-distance calculated on surface-form or some variation on it (stem, lemma), which does not take into consideration any semantic aspects in matching. This paper presents a novel and efficient approach to incorporating semantic information in the form of paraphrasing in the edit-distance metric. The approach computes edit-distance while efficiently considering paraphrases using dynamic programming and greedy approximation. In addition to using automatic evaluation metrics like BLEU and METEOR, we have carried out an extensive human evaluation in which we measured post-editing time, keystrokes, HTER, HMETEOR, and carried out three rounds of subjective evaluations. Our results show that paraphrasing substantially improves TM matching and retrieval, resulting in translation performance increases when translators use paraphrase-enhanced TMs.
    • Linguistic features of genre and method variation in translation: A computational perspective

      Lapshinova-Koltunski, Ekaterina; Zampieri, Marcos; Legallois, Dominique; Charnois, Thierry; Larjavaara, Meri (Mouton De Gruyter, 2018-04-09)
      In this contribution we describe the use of text classification methods to investigate genre and method variation in an English - German translation corpus. For this purpose we use linguistically motivated features representing texts using a combination of part-of-speech tags arranged in bigrams, trigrams, and 4-grams. The classification method used in this study is a Bayesian classifier with Laplace smoothing. We use the output of the classifiers to carry out an extensive feature analysis on the main difference between genres and methods of translation.
    • Predicting the Type and Target of Offensive Posts in Social Media

      Zampieri, Marcos; Malmasi, Shervin; Nakov, Preslav; Rosenthal, Sara; Farra, Noura; Kumar, Ritesh (Association for Computational Linguistics, 2019-06-01)
      As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.
    • A report on the Third VarDial evaluation campaign

      Zampieri, Marcos; Malmasi, Shervin; Scherrer, Yves; Samardžić, Tanja; Tyers, Francis; Silfverberg, Miikka; Klyueva, Natalia; Pan, Tung-Le; Huang, Chu-Ren; Ionescu, Radu Tudor; et al. (Association for Computational Linguistics, 2019-12-31)
      In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run – German Dialect Identification (GDI) – and four new tasks – Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.