This paper reports results in building an Egyptian Arabic speech recognition system as an example for under-resourced languages. We investigated different approaches to build the system using 10 hours for training the acoustic model, and results for both grapheme system and phoneme system using MADA. The phoneme-based system shows better results than the grapheme-based system. In this paper, we explore the use of tweets written in dialectal Arabic. Using 880K Egyptian tweets reduced the Out Of Vocabulary (OOV) rate from 15.1% to 3.2% and the WER from 59.6% to 44.7%, a relative gain 25% in WER.

In simultaneous interpreting, human experts incrementally construct and extend partial hypotheses about the source speaker’s message, and start to verbalize a corresponding message in the target language, based on a partial translation – which may have to be corrected occasionally. They commence the target utterance in the hope that they will be able to finish understanding the source speaker’s message and determine its translation in time for the unfolding delivery. Of course, both incremental understanding and translation by humans can be garden-pathed, although experts are able to optimize their delivery so as to balance the goals of minimal latency, translation quality and high speech fluency with few corrections. We investigate the temporal properties of both translation input and output to evaluate the tradeoff between low latency and translation quality. In addition, we estimate the improvements that can be gained with a tempo-elastic speech synthesizer.

Word Confidence Estimation (WCE) for machine translation (MT) or automatic speech recognition (ASR) consists in judging each word in the (MT or ASR) hypothesis as correct or incorrect by tagging it with an appropriate label. In the past, this task has been treated separately in ASR or MT contexts and we propose here a joint estimation of word confidence for a spoken language translation (SLT) task involving both ASR and MT. This research work is possible because we built a specific corpus which is first presented. This corpus contains 2643 speech utterances for which a quintuplet containing: ASR output (src-asr), verbatim transcript (src-ref), text translation output (tgt-mt), speech translation output (tgt-slt) and post-edition of translation (tgt-pe), is made available. The rest of the paper illustrates how such a corpus (made available to the research community) can be used for evaluating word confidence estimators in ASR, MT or SLT scenarios. WCE for SLT could help rescoring SLT output graphs, improving translators productivity (for translation of lectures or movie subtitling) or it could be useful in interactive speech-to-speech translation scenarios.

Translating meetings presents a challenge since multi-speaker speech shows a variety of disfluencies. In this paper we investigate the importance of transforming speech into well-written input prior to translating multi-party meetings. We first analyze the characteristics of this data and establish oracle scores. Sentence segmentation and punctuation are performed using a language model, turn information, or a monolingual translation system. Disfluencies are removed by a CRF model trained on in-domain and out-of-domain data. For comparison, we build a combined CRF model for punctuation insertion and disfluency removal. By applying these models, multi-party meetings are transformed into fluent input for machine translation. We evaluate the models with regard to translation performance and are able to achieve an improvement of 2.1 to 4.9 BLEU points depending on the availability of turn information.

We conduct dependency-based head finalization for statistical machine translation (SMT) for Myanmar (Burmese). Although Myanmar is an understudied language, linguistically it is a head-final language with similar syntax to Japanese and Korean. So, applying the efficient techniques of Japanese and Korean processing to Myanmar is a natural idea. Our approach is a combination of two approaches. The first is a head-driven phrase structure grammar (HPSG) based head finalization for English-to-Japanese translation, the second is dependency-based pre-ordering originally designed for English-to-Korean translation. We experiment on Chinese-, English-, and French-to-Myanmar translation, using a statistical pre-ordering approach as a comparison method. Experimental results show the dependency-based head finalization was able to consistently improve a baseline SMT system, for different source languages and different segmentation schemes for the Myanmar language.

In this paper we explore various adaptation techniques for continuous space translation models (CSTMs). We consider the following practical situation: given a large scale, state-of-the-art SMT system containing a CSTM, the task is to adapt the CSTM to a new domain using a (relatively) small in-domain parallel corpus. Our method relies on the definition of a new discriminative loss function for the CSTM that borrows from both the max-margin and pair-wise ranking approaches. In our experiments, the baseline out-of-domain SMT system is initially trained for the WMT News translation task, and the CSTM is to be adapted to the lecture translation task as defined by IWSLT evaluation campaign. Experimental results show that an improvement of 1.5 BLEU points can be achieved with the proposed adaptation method.

We introduce two methods to collect additional training data for statistical machine translation systems from public social network content. The first method identifies multilingual content where the author self-translated their own post to reach additional friends, fans or customers. Once identified, we can split the post in the language segments and extract translation pairs from this content. The second methods considers web links (URLs) that users add as part of their post to point the reader to a video, article or website. If the same URL is shared from different language users, there is a chance they might give the same comment in their respective language. We use a support vector machine (SVM) as a classifier to identify true translations from all candidate pairs. We collected additional translation pairs using both methods for the language pairs Spanish-English and Portuguese-English. Testing the collected data as additional training data for statistical machine translations on in-domain test sets resulted in very significant improvements of up to 5 BLEU.

In this paper we explore segmentation strategies for the stream decoder a method for decoding from a continuous stream of input tokens, rather than the traditional method of decoding from sentence segmented text. The behavior of the decoder is analyzed and modifications to the decoding algorithm are proposed to improve its performance. The experimental results show our proposed decoding strategies to be effective, and add support to the original findings that this approach is capable of approaching the performance of the underlying phrase-based machine translation decoder, at useful levels of latency. Our experiments evaluated the stream decoder on a broader set of language pairs than in previous work. We found most European language pairs were similar in character, and report results on English-Chinese and English-German pairs which are of interest due to the reordering required.

Statistical Machine Translation produces results that make it a competitive option in most machine-assisted translation scenarios. However, these good results often come at a very high computational cost and correspond to training regimes which are unfit to many practical contexts, where the ability to adapt to users and domains and to continuously integrate new data (eg. in post-edition contexts) are of primary importance. In this article, we show how these requirements can be met using a strategy for on-demand word alignment and model estimation. Most remarkably, our incremental system development framework is shown to deliver top quality translation performance even in the absence of tuning, and to surpass a strong baseline when performing online tuning. All these results obtained with great computational savings as compared to conventional systems.

In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. By using this approach in a state-of-the-art translation system, we can improve the performance by up to 0.5 BLEU points for three different language pairs on the TED translation task.

Conversational spoken language translation (CSLT) systems facilitate bilingual conversations in which the two participants speak different languages. Bilingual conversations provide additional contextual information that can be used to improve the underlying machine translation system. In this paper, we describe a novel translation model adaptation method that anticipates a participant’s response in the target language, based on his counterpart’s prior turn in the source language. Our proposed strategy uses the source language utterance to perform cross-language retrieval on a large corpus of bilingual conversations in order to obtain a set of potentially relevant target responses. The responses retrieved are used to bias translation choices towards anticipated responses. On an Iraqi-to-English CSLT task, our method achieves a significant improvement over the baseline system in terms of BLEU, TER and METEOR metrics.

Standard SMT decoders operate by translating disjoint spans of input words, thus discarding information in form of overlapping phrases that is present at phrase extraction time. The use of overlapping phrases in translation may enhance fluency in positions that would otherwise be phrase boundaries, they may provide additional statistical support for long and rare phrases, and they may generate new phrases that have never been seen in the training data. We show how to extract overlapping phrases offline for hierarchical phrasebased SMT, and how to extract features and tune weights for the new phrases. We find gains of 0.3 − 0.6 BLEU points over discriminatively trained hierarchical phrase-based SMT systems on two datasets for German-to-English translation.

Translation of the output of automatic speech recognition (ASR) systems, also known as speech translation, has received a lot of research interest recently. This is especially true for programs such as DARPA BOLT which focus on improving spontaneous human-human conversation across languages. However, this research is hindered by the dearth of datasets developed for this explicit purpose. For Egyptian Arabic-English, in particular, no parallel speechtranscription-translation dataset exists in the same domain. In order to support research in speech translation, we introduce the Callhome Egyptian Arabic-English Speech Translation Corpus. This supplements the existing LDC corpus with four reference translations for each utterance in the transcripts. The result is a three-way parallel dataset of Egyptian Arabic Speech, transcriptions and English translations.

Finding sufficient in-domain text data for language modeling is a recurrent challenge. Some methods have already been proposed for selecting parts of out-of-domain text data most closely resembling the in-domain data using a small amount of the latter. Including this new “near-domain” data in training can potentially lead to better language model performance, while reducing training resources relative to incorporating all data. One popular, state-of-the-art selection process based on cross-entropy scores makes use of in-domain and out-ofdomain language models. In order to compensate for the limited availability of the in-domain data required for this method, we introduce enhancements to two of its steps. Firstly, we improve the procedure for drawing the outof-domain sample data used for selection. Secondly, we use word-associations in order to extend the underlying vocabulary of the sample language models used for scoring. These enhancements are applied to selecting text for language modeling of talks given in a technical subject area. Besides comparing perplexity, we judge the resulting language models by their performance in automatic speech recognition and machine translation tasks. We evaluate our method in different contexts. We show that it yields consistent improvements, up to 2% absolute reduction in word error rate and 0.3 Bleu points. We achieve these improvements even given a much smaller in-domain set.

Previous work has shown that training the neural networks for bottle neck feature extraction in a multilingual way can lead to improvements in word error rate and average term weighted value in a telephone key word search task. In this work we conduct a systematic study on a) which multilingual training strategy to employ, b) the effect of language selection and amount of multilingual training data used and c) how to find a suitable combination for languages. We conducted our experiment on the key word search task and the languages of the IARPA BABEL program. In a first step, we assessed the performance of a single language out of all available languages in combination with the target language. Based on these results, we then combined a multitude of languages. We also examined the influence of the amount of training data per language, as well as different techniques for combining the languages during network training. Our experiments show that data from arbitrary additional languages does not necessarily increase the performance of a system. But when combining a suitable set of languages, a significant gain in performance can be achieved.

Syntactic parsing is a fundamental natural language processing technology that has proven useful in machine translation, language modeling, sentence segmentation, and a number of other applications related to speech translation. However, there is a paucity of manually annotated syntactic parsing resources for speech, and particularly for the lecture speech that is the current target of the IWSLT translation campaign. In this work, we present a new manually annotated treebank of TED talks that we hope will prove useful for investigation into the interaction between syntax and these speechrelated applications. The first version of the corpus includes 1,217 sentences and 23,158 words manually annotated with parse trees, and aligned with translations in 26-43 different languages. In this paper we describe the collection of the corpus, and an analysis of its various characteristics.

Punctuation prediction is an important task in spoken language translation and can be performed by using a monolingual phrase-based translation system to translate from unpunctuated to text with punctuation. However, a punctuation prediction system based on phrase-based translation is not able to capture long-range dependencies between words and punctuation marks. In this paper, we propose to employ hierarchical translation in place of phrase-based translation and show that this approach is more robust for unseen word sequences. Furthermore, we analyze different optimization criteria for tuning the scaling factors of a monolingual statistical machine translation system. In our experiments, we compare the new approach with other punctuation prediction methods and show improvements in terms of F1-Score and BLEU on the IWSLT 2014 German→English and English→French translation tasks.

We propose a novel data-driven rule-based preordering approach, which uses the tree information of multiple syntactic levels. This approach extend the tree-based reordering from one level into multiple levels, which has the capability to process more complicated reordering cases. We have conducted experiments in English-to-Chinese and Chinese-to-English translation directions. Our results show that the approach has led to improved translation quality both when it was applied separately or when it was combined with some other reordering approaches. As our reordering approach was used alone, it showed an improvement of 1.61 in BLEU score in the English-to-Chinese translation direction and an improvement of 2.16 in BLEU score in the Chinese-to-English translation direction, in comparison with the baseline, which used no word reordering. As our preordering approach were combined with the short rule [1], long rule [2] and tree rule [3] based preordering approaches, it showed further improvements of up to 0.43 in BLEU score in the English-to-Chinese translation direction and further improvements of up to 0.3 in BLEU score in the Chinese-to-English translation direction. Through the translations that used our preordering approach, we have also found many translation examples with improved syntactic structures.