Neural Machine Translation by Generating Multiple Linguistic Factors

Factored neural machine translation (FNMT) is founded on the idea of using the morphological and grammatical decomposition of the words (factors) at the output side of the neural network. This architecture addresses two well-known problems occurring in MT, namely the size of target language vocabulary and the number of unknown tokens produced in the translation. FNMT system is designed to manage larger vocabulary and reduce the training time (for systems with equivalent target language vocabulary size). Moreover, we can produce grammatically correct words that are not part of the vocabulary. FNMT model is evaluated on IWSLT'15 English to French task and compared to the baseline word-based and BPE-based NMT systems. Promising qualitative and quantitative results (in terms of BLEU and METEOR) are reported.Comment: 11 pages, 3 figues, SLSP conferenc

Influence of Morphological Features on Language Modeling With Neural Networks in Speech Recognition Systems

Automatsko prepoznavanje govora je tehnologija koja računarima omogućava pretvaranje izgovorenih reči u tekst. Ona se može primeniti u mnogim savremenim sistemima koji uključuju komunikaciju između čoveka i mašine. U ovoj disertaciji detaljno je opisana jedna od dve glavne komponente sistema za prepoznavanje govora, a to je jezički model, koji specificira rečnik sistema, kao i pravila prema kojim se pojedinačne reči mogu povezati u rečenicu. Srpski jezik spada u grupu visoko inflektivnih i morfološki bogatih jezika, što znači da koristi veći broj različitih završetaka reči za izražavanje željene gramatičke, sintaksičke ili semantičke funkcije date reči. Ovakvo ponašanje često dovodi do velikog broja grešaka sistema za prepoznavanje govora kod kojih zbog dobrog akustičkog poklapanja prepoznavač pogodi osnovni oblik reči, ali pogreši njen završetak. Taj završetak može da označava drugu morfološku kategoriju, na primer, padež, rod ili broj. U radu je predstavljen novi alat za modelovanje jezika, koji uz identitet reči u modelu može da koristi dodatna leksička i morfološka obeležja reči, čime je testirana hipoteza da te dodatne informacije mogu pomoći u prevazilaženju značajnog broja grešaka prepoznavača koje su posledica inflektivnosti srpskog jezika.Automatic speech recognition is a technology that allows computers to convert spoken words into text. It can be applied in various areas which involve communication between humans and machines. This thesis primarily deals with one of two main components of speech recognition systems - the language model, that specifies the vocabulary of the system, as well as the rules by which individual words can be linked into sentences. The Serbian language belongs to a group of highly inflective and morphologically rich languages, which means that it uses a number of different word endings to express the desired grammatical, syntactic, or semantic function of the given word. Such behavior often leads to a significant number of errors in speech recognition systems where due to good acoustic matching the recognizer correctly guesses the basic form of the word, but an error occurs in the word ending. This word ending may indicate a different morphological category, for example, word case, grammatical gender, or grammatical number. The thesis presents a new language modeling tool which, along with the word identity, can also model additional lexical and morphological features of the word, thus testing the hypothesis that this additional information can help overcome a significant number of recognition errors that result from the high inflectivity of the Serbian language