We present a pairwise learning-to-rank approach to machine translation evaluation that learns to differentiate better from worse translations in the context of a given reference. We integrate several layers of linguistic information encapsulated in tree-based structures, making use of both the reference and the system output simultaneously, thus bringing our ranking closer to how humans evaluat...

Automatically detecting similar Chinese characters is useful in many areas, such as building intelligent authoring tools (e. g. automatic multiple choice question generation) in the area of computer assisted language learning. Previous work on the computation of Chinese character similarity focused on detecting character glyph similarity while ignored the importance of other character features,...

This paper proposes a novel automatic pronunciation scoring framework using learning to rank. Human scores of the utterances are treated as ranks and are used as the ranking ground truths. Scores generated from various existing scoring methods are used as the features to train the learning to rank function. The output of the function is then segmented by the proposed DP-based method and hence b...

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman’s ρ to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggreg...

This paper presents an ongoing research in utilizing content quality to enhance thread and post retrieval tasks in support oriented forums. First, we adapt the established information quality approach as a theoretical way to understand quality. Then, we outline research areas that help in quantifying quality. Lastly, we propose to use learning to rank method to weight quality parameters while r...