Results beat forgetron. Expensive? I always knew keeping all xi to represent w was a waste! At most N should be enough.

We present a novel commentator system that learns language from sportscasts of simulated soccer games. The system learns to parse and generate commentaries without any engineered knowledge about the English language. Training is done using only ambiguous supervision in the form of textual human commentaries and simulation states of the soccer games. The system simultaneously tries to establish correspondences between the commentaries and the simulation states as well as build a translation model. We also present a novel algorithm, Iterative Generation Strategy Learning (IGSL), for deciding which events to comment on. Human evaluations of the generated commentaries indicate they are of reasonable quality compared to human commentaries.

Professional translators of technical documents often use Translation Memory (TM) systems in order to capitalize on the repetitions frequently observed in these documents. TM systems typically exploit not only complete matches between the source sentence to be translated and some previously translated sentence, but also so-called \emph{fuzzy matches}, where the source sentence has some substantial commonality with a previously translated sentence. These fuzzy matches can be very worthwhile as a starting point for the human translator, but the translator then needs to manually edit the associated TM-based translation to accommodate the differences with the source sentence to be translated. If part of this process could be automated, the cost of human translation could be significantly reduced. The paper proposes to perform this automation in the following way: a phrase-based Statistical Machine Translation (SMT) system (trained on a bilingual corpus in the same domain as the TM) is combined with the TM fuzzy match, by extracting from the fuzzy-match a large (possibly gapped) bi-phrase that is dynamically added to the usual set of ''static'' bi-phrases used for decoding the source. We report experiments that show significant improvements in terms of BLEU and NIST scores over both the translations produced by the stand-alone SMT system and the fuzzy-match translations proposed by the stand-alone TM system.

This paper presents a language-independent context-based sentence alignment technique given parallel corpora. We can view the problem of aligning sentences as finding translations of sentences chosen from different sources. Unlike current approaches which rely on pre-defined features and models, our algorithm employs features derived from the distributional properties of words and does not use any language dependent knowledge. We make use of the context of sentences and the notion of Zipfian word vectors which effectively models the distributional properties of words in a given sentence. We accept the context to be the frame in which the reasoning about sentence alignment is done. We evaluate the performance of our system based on two different measures: sentence alignment accuracy and sentence alignment coverage. We compare the performance of our system with commonly used sentence alignment systems and show that our system performs 1.2149 to 1.6022 times better in reducing the error rate in alignment accuracy and coverage for moderately sized corpora.