Marianna Apidianaki

Alignment-based sense selection in METEOR and the RATATOUILLE recipe

This paper describes Meteor-WSD and RATATOUILLE, the LIMSI submissions to the WMT15 metrics shared task. MeteorWSD extends synonym mapping to languages other than English based on alignments and gives credit to semantically adequate translations in context. We show that context-sensitive synonym selection increases the correlation of the Meteor metric with human judgments of translation quality on the WMT14 data. RATATOUILLE combines MeteorWSD with nine other metrics for evaluation and outperforms the best metric (BEER) involved in its computation.

LIMSI: Translations as Source of Indirect Supervision for Multilingual All-Words Sense Disambiguation and Entity Linking

We present the LIMSI submission to the Multilingual Word Sense Disambiguation and Entity Linking task of SemEval-2015. The system exploits the parallelism of the multilingual test data and uses translations as source of indirect supervision for sense selection. The LIMSI system gets best results in English in all domains and shows that alignment information can successfully guide disambiguation. This simple but effective method can serve to generate high quality sense annotated data for WSD system training.

Vector-space models for PPDB paraphrase ranking in context.

The PPDB is an automatically built database which contains millions of paraphrases in different languages. Paraphrases in this resource are associated with features that serve to their ranking and reflect paraphrase quality. This context-unaware ranking captures the semantic similarity of paraphrases but cannot serve to estimate their adequacy in specific contexts. We propose to use vector-space semantic models for selecting PPDB paraphrases that preserve the meaning of specific text fragments. This is the first work that addresses the substitutability of PPDB paraphrases in context. We show that vector-space models of meaning can be successfully applied to this task and increase the benefit brought by the use of the PPDB resource in applications.

METEOR-WSD: Improved Sense Matching in MT Evaluation

We present an initial experiment in integrating a disambiguation step in MT evaluation. We show that accounting for sense distinctions helps METEOR establish better sense correspondences and improves its correlation with human judgments of translation quality.

Mapping the Paraphrase Database to WordNet

WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic structure of WordNet that would make it more directly useful for downstream tasks. We present a method for mapping words from PPDB to WordNet synsets with 89% accuracy. The mapping also lays important groundwork for incorporating WordNet’s relations into PPDB so as to increase its utility for semantic reasoning in applications.

Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.

Word Sense Filtering Improves Embedding-Based Lexical Substitution

The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense. We show that a filtering mechanism based on a sense inventory optimized for substitutability can improve the results of these models. Our sense inventory is constructed using a clustering method which generates paraphrase clusters that are congruent with lexical substitution annotations in a development set. The results show that lexical substitution can still benefit from senses which can improve the output of vector space paraphrase ranking models.

TransRead: Designing a Bilingual Reading Experience with Machine Translation Technologies

In this paper, we use multilingual Natural Language Processing (NLP) tools to improve the reading experience of parallel texts on mobile devices. Such enterprise poses multiple challenging issues both from the NLP and from the Human Computer Interaction (HCI) perspectives. We discuss these problems, and report on our own solutions, now implemented in a full-fledged bilingual reading device.