This paper presents the TRL team's system submitted for the CoNLL 2017 Shared Task, "Multilingual Parsing from Raw Text to Universal Dependencies." We ran the system for all languages with our own fully pipelined components without relying on either pre-trained baseline or machine learning techniques. We used only the universal part-of-speech tags and distance between words, and applied deterministic rules to assign labels. The delexicalized models are suitable for crosslingual transfer or universal approaches. Experimental results show that our model performed well in some metrics and leads discussion on topics such as contribution of each component and on syntactic similarities among languages.

No abstract

We propose a new task called image-to-text matching (ITeM) to facilitate multimodal document understanding. ITeM requires a system to learn a plausible assignment of images to texts in a multimodal document. To study this task, we systematically construct a dataset comprising 66,947 documents with 320,200 images from Wikipedia. We evaluate two existing state-of-the-art multimodal systems on our task to assess the validity and difficulty of our task. Experimental results show that the systems greatly outperform simple baselines while their performances are still far from that of humans. Further, the proposed task does not contribute significantly to the existing multimodal tasks; however, detailed analysis suggests that the task becomes more complex when more images are present in a document and that the proposed task can offer a new capability for image-to-text understanding not achievable through existing tasks, such as multiple image consideration or image abstraction.

This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese. Unlike the transition-and graph-based algorithms in most state-of-the-art parsers, our parser directly selects the head word of a dependent from a limited number of candidates. This method drastically simplifies the model so that we can easily interpret the output of the neural model. Moreover, by exploiting grammatical knowledge to restrict possible modification types, we can control the output of the parser to reduce specific errors without adding annotated corpora. The neural parser performed well both on conventional Japanese corpora and the Japanese version of Universal Dependency corpus, and the advantages of distributed representations were observed in the comparison with the non-neural conventional model.

We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task. Although it has been shown that the Distributional Informativeness Hypothesis (DIH) holds on text, in which the DIH assumes that a context surrounding a hyponym is more informative than that of a hypernym, it has never been tested on visual objects. Since our perception is tightly associated with language, it is meaningful to explore whether the DIH holds on visual objects. To this end, we consider visual objects as the context of a word and represent a word as a bag of visual objects found in images associated with the word. This allows us to test the feasibility of the visual DIH. To better distinguish word pairs in a hypernym relation from other relations such as co-hypernyms, we also propose a new measurable function that takes into account both the difference in the generality of meaning and similarity of meaning between words. Our experimental results show that the DIH holds on visual objects and that the proposed method combined with the proposed function outperforms existing unsupervised representation methods. * Note that the number of search trials differs among the datasets because hrel(x, y), the function for detecting word pairs in a hypernym relation, is applied only to the WBLESS and BIBLESS datasets.