Background research is an essential part of document writing.Search engines are great for retrieving information once we know what to look for.However, the bigger challenge is often identifying topics for further research.Automated tools could help significantly in this discovery process and increase the productivity of the writer.In this paper, we formulate the problem of recommending topics to a writer.We consider this as a supervised learning problem and run a user study to validate this approach.We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set.We demonstrate how a simple modification of the crossentropy loss function leads to improved results of the deep learning models.

This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it presents a decoding algorithm for confusion networks which results as an extension of a state-of-the-art phrase-based text translation decoder. The confusion network decoder significantly improves both in efficiency and performance over previous work along this direction, and outperforms the background text translation system. Experimental results in terms of translation accuracy and decoding efficiency are reported for the task of translating plenary speeches of the European Parliament from Spanish to english and from english to Spanish.

This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it presents an implementation of a confusion network decoder which significantly improves both in efficiency and performance previous work along this direction. The confusion network decoder results as an extension of a state-of-the-art phrase-based text translation system. Experimental results in terms of decoding speed and translation accuracy are reported on a real-data task, namely the translation of plenary speeches at the European Parliament from Spanish to English.

In this paper we present the ongoing work at RWTH Aachen University for building a speech-to-speech translation system within the TC-Star project. The corpus we work on consists of parliamentary speeches held in the European Plenary Sessions. To our knowledge, this is the first project that focuses on speech-to-speech translation applied to a real-life task. We describe the statistical approach used in the development of our system and analyze its performance under different conditions: dealing with syntactically correct input, dealing with the exact transcription of speech and dealing with the (noisy) output of an automatic speech recognition system. Experimental results show that our system is able to perform adequately in each of these conditions.

In statistical machine translation, the generation of a translation hypothesis is computationally expensive. If arbitrary word-reorderings are permitted, the search problem is NP-hard. On the other hand, if we restrict the possible word-reorderings in an appropriate way, we obtain a polynomial-time search algorithm.In this paper, we compare two different reordering constraints, namely the ITG constraints and the IBM constraints. This comparison includes a theoretical discussion on the permitted number of reorderings for each of these constraints. We show a connection between the ITG constraints and the since 1870 known Schröder numbers.We evaluate these constraints on two tasks: the Verbmobil task and the Canadian Hansards task. The evaluation consists of two parts: First, we check how many of the Viterbi alignments of the training corpus satisfy each of these constraints. Second, we restrict the search to each of these constraints and compare the resulting translation hypotheses.The experiments will show that the baseline ITG constraints are not sufficient on the Canadian Hansards task. Therefore, we present an extension to the ITG constraints. These extended ITG constraints increase the alignment coverage from about 87% to 96%.