An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.

However large a hand-crafted widecoverage grammar is, there are always going to be words and constructions that are not included in it and are going to cause parse failure. Due to their heterogeneous and flexible nature, Multiword Expressions (MWEs) provide an endless source of parse failures. As the number of such expressions in a speaker's lexicon is equiparable to the number of single word units (Jackendoff, 1997), one major challenge for robust natural language processing systems is to be able to deal with MWEs. In this paper we propose to semi-automatically detect MWE candidates in texts using some error mining techniques and validating them using a combination of the World Wide Web as a corpus and some statistical measures. For the remaining candidates possible lexicosyntactic types are predicted, and they are subsequently added to the grammar as new lexical entries. This approach provides a significant increase in the coverage of these expressions.

No abstract

This article describes a linguistically informed method for integrating phrasal verbs into statistical machine translation (SMT) systems. In a case study involving English to Bulgarian SMT, we show that our method does not only improve translation quality but also outperforms similar methods previously applied to the same task. We attribute this to the fact that, in contrast to previous work on the subject, we employ detailed linguistic information. We found out that features which describe phrasal verbs as idiomatic or compositional contribute most to the better translation quality achieved by our method.

Many papers are chasing state-of-the-art (SOTA) numbers, and more will do so in the future. SOTA-chasing comes with many costs. SOTA-chasing squeezes out more promising opportunities such as coopetition and interdisciplinary collaboration. In addition, there is a risk that too much SOTA-chasing could lead to claims of superhuman performance, unrealistic expectations, and the next AI winter. Two root causes for SOTA-chasing will be discussed: (1) lack of leadership and (2) iffy reviewing processes. SOTA-chasing may be similar to the replication crisis in the scientific literature. The replication crisis is yet another example, like evaluation, of over-confidence in accepted practices and the scientific method, even when such practices lead to absurd consequences.