Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity.In contrast, we demonstrate that word-weighted averaging can encode argument representation which can incorporate word pair information efficiently. By saving an order of magnitude in parameters, our proposed model achieves equivalent performance, but trains seven times faster.

Previous studies on Thai Sentence Boundary Detection (SBD) mostly assumed sentence ends at a space disambiguation problem, which classified space either as an indicator for Sentence Boundary (SB) or non-Sentence Boundary (nSB). In this paper, we propose a word labeling approach which treats space as a normal word, and detects SB between any two words. This removes the restriction for SB to be oc-curred only at space and makes our system more robust for modern Thai writing. It is because in modern Thai writing, space is not consistently used to indicate SB. As syntactic information contributes to better SBD, we further propose a joint Part-Of-Speech (POS) tagging and SBD framework based on Factorial Conditional Random Field (FCRF) model. We compare the performance of our proposed ap-proach with reported methods on ORCHID corpus. We also performed experiments of FCRF model on the TaLAPi corpus. The results show that the word labelling approach has better performance than pre-vious space-based classification approaches and FCRF joint model outperforms LCRF model in terms of SBD in all experiments.

Punctuation prediction and disfluency prediction can improve downstream natural language processing tasks such as machine translation and information extraction.Combining the two tasks can potentially improve the efficiency of the overall pipeline system and reduce error propagation.In this work 1 , we compare various methods for combining punctuation prediction (PU) and disfluency prediction (DF) on the Switchboard corpus.We compare an isolated prediction approach with a cascade approach, a rescoring approach, and three joint model approaches.For the cascade approach, we show that the soft cascade method is better than the hard cascade method.We also use the cascade models to generate an n-best list, use the bi-directional cascade models to perform rescoring, and compare that with the results of the cascade models.For the joint model approach, we compare mixedlabel Linear-chain Conditional Random Field (LCRF), cross-product LCRF and 2layer Factorial Conditional Random Field (FCRF) with soft-cascade LCRF.Our results show that the various methods linking the two tasks are not significantly different from one another, although they perform better than the isolated prediction method by 0.5-1.5% in the F1 score.Moreover, the clique order of features also shows a marked difference.

Recently, a new technology called Haptic Voice Recognition (HVR) was proposed to enhance the speech recognition efficiency and accuracy for modern mobile devices, which has been successfully applied for robust English voice recognition. As both Pinyin and handwriting input methods work quite slow in mobile devices because of typing errors and ambiguity, it is interesting to apply this technology to assist Mandarin predictive text input. However, it is not straightforward because the characteristics of Mandarin significantly differ from alphabetic western languages. In this paper, we investigated what possible haptic inputs are important and how these information can be incorporated to improve Mandarin text input. Various experiments were conducted and results have shown that with the help of acoustic and tonal information, the ambiguity of Pinyin Initial based Mandarin predictive text input is largely reduced and an oracle character error rate of 3.8% for the top 4 candidates could be achieved, which is usually the number of word candidates displayed on mobile devices. Our Mandarin HVR system has also shown its robustness in noisy environments.

13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012