Fluent Translations from Disfluent Speech in End-to-End Speech Translation

Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies. With the rise of end-to-end speech translation models, processing steps such as disfluency removal that were previously an intermediate step between speech recognition and machine translation need to be incorporated into model architectures. We use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected `copy-edited' references for the Fisher Spanish-English dataset. We are able to directly generate fluent translations and introduce considerations about how to evaluate success on this task. This work provides a baseline for a new task, the translation of conversational speech with joint removal of disfluencies.Comment: Accepted at NAACL 201

Syntactic discriminative language model rerankers for statistical machine translation

This article describes a method that successfully exploits syntactic features for n-best translation candidate reranking using perceptrons. We motivate the utility of syntax by demonstrating the superior performance of parsers over n-gram language models in differentiating between Statistical Machine Translation output and human translations. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST’s MT-Eval benchmarks. While deep features extracted from parse trees do not consistently help, we show how features extracted from a shallow Part-of-Speech annotation layer outperform a competitive baseline and a state-of-the-art comparative reranking approach, leading to significant BLEU improvements on three different test sets

From Disfluency Detection to Intent Detection and Slot Filling

We present the first empirical study investigating the influence of disfluency detection on downstream tasks of intent detection and slot filling. We perform this study for Vietnamese -- a low-resource language that has no previous study as well as no public dataset available for disfluency detection. First, we extend the fluent Vietnamese intent detection and slot filling dataset PhoATIS by manually adding contextual disfluencies and annotating them. Then, we conduct experiments using strong baselines for disfluency detection and joint intent detection and slot filling, which are based on pre-trained language models. We find that: (i) disfluencies produce negative effects on the performances of the downstream intent detection and slot filling tasks, and (ii) in the disfluency context, the pre-trained multilingual language model XLM-R helps produce better intent detection and slot filling performances than the pre-trained monolingual language model PhoBERT, and this is opposite to what generally found in the fluency context.Comment: In Proceedings of INTERSPEECH 202

Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives

Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives influencing Spoken Language Understanding (SLU) on these speech events. This aim of this article is to synthesise a breadth of perspectives in a holistic way; i.e. from considering underlying (psycho)linguistic theory, to their annotation and consideration in Automatic Speech Recognition (ASR) and SLU systems, to lastly, their study from a generation standpoint. This article aims to present the perspectives in an approachable way to the SLU and Conversational AI community, and discuss moving forward, what we believe are the trends and challenges in each area.Comment: To appear in TAL Journa

Procjena kvalitete strojnog prijevoda govora: studija slučaja aplikacije ILA

Machine translation (MT) is becoming qualitatively more successful and quantitatively more productive at an unprecedented pace. It is becoming a widespread solution to the challenges of a constantly rising demand for quick and affordable translations of both text and speech, causing disruption and adjustments of the translation practice and profession, but at the same time making multilingual communication easier than ever before. This paper focuses on the speech-to-speech (S2S) translation app Instant Language Assistant (ILA), which brings together the state-of-the-art translation technology: automatic speech recognition, machine translation and text-to-speech synthesis, and allows for MT-mediated multilingual communication. The aim of the paper is to assess the quality of translations of conversational language produced by the S2S translation app ILA for en-de and en-hr language pairs. The research includes several levels of translation quality analysis: human translation quality assessment by translation experts using the Fluency/Adequacy Metrics, light-post editing, and automated MT evaluation (BLEU). Moreover, the translation output is assessed with respect to language pairs to get an insight into whether they affect the MT output quality and how. The results show a relatively high quality of translations produced by the S2S translation app ILA across all assessment models and a correlation between human and automated assessment results.Strojno je prevođenje sve kvalitetnije i sve je više prisutno u svakodnevnom životu. Zbog porasta potražnje za brzim i pristupačnim prijevodima teksta i govora, strojno se prevođenje nameće kao općeprihvaćeno rješenje, što dovodi do korjenitih promjena i prilagodbi u prevoditeljskoj struci i praksi te istodobno višejezičnu komunikaciju čini lakšom nego ikada do sada. Ovaj se rad bavi aplikacijom Instant Language Assistant (ILA) za strojni prijevod govora. ILA omogućuje višejezičnu komunikaciju posredovanu strojnim prevođenjem, a temelji se na najnovijim tehnološkim dostignućima, i to na automatskom prepoznavanju govora, strojnom prevođenju i sintezi teksta u govor. Cilj je rada procijeniti kvalitetu prijevoda razgovornog jezika dobivenog pomoću aplikacije ILA i to za parove jezika engleski – njemački te engleski – hrvatski. Kvaliteta prijevoda analizira se u nekoliko faza: kvalitetu prijevoda procjenjuju stručnjaci pomoću metode procjene tečnosti i točnosti (engl. Fluency/Adequacy Metrics), zatim se provodi ograničena redaktura strojno prevedenih govora (engl. light post-editing), nakon čega slijedi automatsko vrednovanje strojnog prijevoda (BLEU). Strojno prevedeni govor procjenjuje se i uzevši u obzir o kojem je jezičnom paru riječ kako bi se dobio uvid u to utječu li jezični parovi na strojni prijevod i na koji način. Rezultati pokazuju da su prijevodi dobiveni pomoću aplikacije ILA za strojni prijevod govora procijenjeni kao razmjerno visokokvalitetni bez obzira na metodu procjene, kao i da se ljudske procjene kvalitete prijevoda poklapaju sa strojnima

Evaluation of the KIT Lecture Translation System

To attract foreign students is among the goals of the Karlsruhe Institute of Technology (KIT). One obstacle to achieving this goal is that lectures at KIT are usually held in German which many foreign students are not sufficiently proficient in, as, e.g., opposed to English. While the students from abroad are learning German during their stay at KIT, it is challenging to become proficient enough in it in order to follow a lecture. As a solution to this problem we offer our automatic simultaneous lecture translation. It translates German lectures into English in real time. While not as good as human interpreters, the system is available at a price that KIT can afford in order to offer it in potentially all lectures. In order to assess whether the quality of the system we have conducted a user study. In this paper we present this study, the way it was conducted and its results. The results indicate that the quality of the system has passed a threshold as to be able to support students in their studies. The study has helped to identify the most crucial weaknesses of the systems and has guided us which steps to take next