Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream language models, it is necessary to add punctuation marks. In this paper, we tackle the punctuation restoration problem specifically for the noisy text (e.g., phone conversation scenarios). To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data. Moreover, we propose a two-stage fine-tuning approach that utilizes the sampled external data as well as our in-domain dataset for models based on BERT. Extensive experiments show that the proposed approach outperforms the baseline with an improvement of 1.12% F1 score.

German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza++ as a reference implementation of the IBM Model 1, an HMMbased alignment and IBM Model 4, we measure the impact of normalizing inflectional morphology on German-English statistical word alignment. We demonstrate that normalizing inflectional morphology improves the perplexity of models and reduces alignment errors.

We approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rules that can be compiled and executed after transfer and before sentence realization in the target language. We measure the reduction in noise in the linguistic representations and the results of human evaluations of end-to-end English-German machine translation.

This paper presents the French implementation of Amalgam, a machine-learned sentence realization system. It presents in some detail two of the machine-learned models employed in Amalgam and shows how linguistic intuition and knowledge can be combined with statistical techniques to improve the performance of the models.

We present an automated, system-internal evaluation technique for linguistic representations in a large-scale, multilingual MT system. We use machine-learned classifiers to recognize the differences between linguistic representations generated from transfer in an MT context from representations that are produced by "native" analysis of the target language. In the MT scenario, convergence of the two is the desired result. Holding the feature set and the learning algorithm constant, the accuracy of the classifiers provides a measure of the overall difference between the two sets of linguistic representations: classifiers with higher accuracy correspond to more pronounced differences between representations. More importantly, the classifiers yield the basis for error-analysis by providing a ranking of the importance of linguistic features. The more salient a linguistic criterion is in discriminating transferred representations from "native" representations, the more work will be needed in order to get closer to the goal of producing native-like MT. We present results from using this approach on the Microsoft MT system and discuss its advantages and possible extensions.