A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate and adapt Cross-View Training (CVT) as a semi-supervised learning technique, allowing us to utilize unlabeled data to improve the model representations. In the Thai sentence segmentation experiments, our model reduced the relative error by 7.4 and 10.5 compared with the baseline models on the Orchid and UGWC datasets, respectively. We also applied our model to the task of pronunciation recovery on the IWSLT English dataset. Our model outperformed the prior sequence tagging models, achieving a relative error reduction of 2.5 . Ablation studies revealed that utilizing n-gram presentations was the main contributing factor for Thai, while the semi-supervised training helped the most for English.
Inserting proper punctuation into Automatic Speech Recognizer(ASR) transcription is a challenging and promising task in real-time Spoken Language Translation(SLT). Traditional methods built on the sequence labelling framework are weak in handling the joint punctuation. To tackle this problem, we propose a novel self-attention based network, which can solve the aforementioned problem very well. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Nets(RNN) and Convolutional Neural Nets(CNN). We conduct extensive experiments on complex punctuation tasks. The experimental results show that the proposed model achieves significant improvements on joint punctuation task while being superior to traditional methods on simple punctuation task as well. Punctuation prediction is an important task in Spoken Language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU. 