Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output.We propose a new general-purpose prediction action which predicts future words in the input to improve quality and minimize delay in simultaneous translation.We train this agent using reinforcement learning with a novel reward function.Our agent with prediction has better translation quality and less delay compared to an agent-based simultaneous translation system without prediction.

The multilingual Paraphrase Database (PPDB) is a freely available automatically created resource of paraphrases in mul-tiple languages. In statistical machine translation, paraphrases can be used to provide translation for out-of-vocabulary (OOV) phrases. In this paper, we show that a graph propagation approach that uses PPDB paraphrases can be used to im-prove overall translation quality. We pro-vide an extensive comparison with previ-ous work and show that our PPDB-based method improves the BLEU score by up to 1.79 percent points. We show that our approach improves on the state of the art in three different settings: when faced with limited amount of parallel training data; a domain shift between training and test data; and handling a morpho-logically complex source language. Our PPDB-based method outperforms the use of distributional profiles from monolin-gual source data. 1

Left-to-right (LR) decoding (Watanabe et al., 2006) is promising decoding algorithm for hierarchical phrase-based translation (Hiero) that visits input spans in arbitrary order producing the output translation in left to right order.This leads to far fewer language model calls, but while LR decoding is more efficient than CKY decoding, it is unable to capture some hierarchical phrase alignments reachable using CKY decoding and suffers from lower translation quality as a result.This paper introduces two improvements to LR decoding that make it comparable in translation quality to CKY-based Hiero.

Hierarchical phrase-based machine translation [1] (Hiero) is a prominent approach for Statistical Machine Translation usually comparable to or better than conventional phrase-based systems. But Hiero typically uses the CKY decoding algorithm which requires the entire input sentence before decoding begins, as it produces the translation in a bottom-up fashion. Left-to-right (LR) decoding [2] is a promising decoding algorithm for Hiero that produces the output translation in left to right order. In this paper we focus on simultaneous translation using the Hiero translation framework. In simultaneous translation, translations are generated incrementally as source language speech input is processed. We propose a novel approach for incremental translation by integrating segmentation and decoding in LR-Hiero. We compare two incremental decoding algorithms for LR-Hiero and present translation quality scores (BLEU) and the latency of generating translations for both decoders on audio lectures from the TED collection.

Left-to-right (LR) decoding (Watanabe et al., 2006b) is a promising decoding algorithm for hierarchical phrase-based translation (Hiero).It generates the target sentence by extending the hypotheses only on the right edge.LR decoding has complexity O(n 2 b) for input of n words and beam size b, compared to O(n 3 ) for the CKY algorithm.It requires a single language model (LM) history for each target hypothesis rather than two LM histories per hypothesis as in CKY.In this paper we present an augmented LR decoding algorithm that builds on the original algorithm in (Watanabe et al., 2006b).Unlike that algorithm, using experiments over multiple language pairs we show two new results: our LR decoding algorithm provides demonstrably more efficient decoding than CKY Hiero, four times faster; and by introducing new distortion and reordering features for LR decoding, it maintains the same translation quality (as in BLEU scores) obtained phrase-based and CKY Hiero with the same translation model.