Xudong Zhang


2023

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SRCB at SemEval-2023 Task 1: Prompt Based and Cross-Modal Retrieval Enhanced Visual Word Sense Disambiguation
Xudong Zhang | Tiange Zhen | Jing Zhang | Yujin Wang | Song Liu
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

The Visual Word Sense Disambiguation (VWSD) shared task aims at selecting the image among candidates that best interprets the semantics of a target word with a short-length phrase for English, Italian, and Farsi. The limited phrase context, which only contains 2-3 words, challenges the model’s understanding ability, and the visual label requires image-text matching performance across different modalities. In this paper, we propose a prompt based and multimodal retrieval enhanced VWSD system, which uses the rich potential knowledge of large-scale pretrained models by prompting and additional text-image information from knowledge bases and open datasets. Under the English situation and given an input phrase, (1) the context retrieval module predicts the correct definition from sense inventory by matching phrase and context through a biencoder architecture. (2) The image retrieval module retrieves the relevant images from an image dataset.(3) The matching module decides that either text or image is used to pair with image labels by a rule-based strategy, then ranks the candidate images according to the similarity score. Our system ranks first in the English track and second in the average of all languages (English, Italian, and Farsi).

2022

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Higher-Order Dependency Parsing for Arc-Polynomial Score Functions via Gradient-Based Methods and Genetic Algorithm
Xudong Zhang | Joseph Le Roux | Thierry Charnois
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We present a novel method for higher-order dependency parsing which takes advantage of the general form of score functions written as arc-polynomials, a general framework which encompasses common higher-order score functions, and includes new ones. This method is based on non-linear optimization techniques, namely coordinate ascent and genetic search where we iteratively update a candidate parse. Updates are formulated as gradient-based operations, and are efficiently computed by auto-differentiation libraries. Experiments show that this method obtains results matching the recent state-of-the-art second order parsers on three standard datasets.

2021

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Strength in Numbers: Averaging and Clustering Effects in Mixture of Experts for Graph-Based Dependency Parsing
Xudong Zhang | Joseph Le Roux | Thierry Charnois
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

We review two features of mixture of experts (MoE) models which we call averaging and clustering effects in the context of graph-based dependency parsers learned in a supervised probabilistic framework. Averaging corresponds to the ensemble combination of parsers and is responsible for variance reduction which helps stabilizing and improving parsing accuracy. Clustering describes the capacity of MoE models to give more credit to experts believed to be more accurate given an input. Although promising, this is difficult to achieve, especially without additional data. We design an experimental set-up to study the impact of these effects. Whereas averaging is always beneficial, clustering requires good initialization and stabilization techniques, but its advantages over mere averaging seem to eventually vanish when enough experts are present. As a by product, we show how this leads to state-of-the-art results on the PTB and the CoNLL09 Chinese treebank, with low variance across experiments.

2020

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Calcul de similarité entre phrases : quelles mesures et quels descripteurs ? (Sentence Similarity : a study on similarity metrics with words and character strings )
Davide Buscaldi | Ghazi Felhi | Dhaou Ghoul | Joseph Le Roux | Gaël Lejeune | Xudong Zhang
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Atelier DÉfi Fouille de Textes

Cet article présente notre participation à l’édition 2020 du Défi Fouille de Textes DEFT 2020 et plus précisément aux deux tâches ayant trait à la similarité entre phrases. Dans notre travail nous nous sommes intéressé à deux questions : celle du choix de la mesure du similarité d’une part et celle du choix des opérandes sur lesquelles se porte la mesure de similarité. Nous avons notamment étudié la question de savoir s’il fallait utiliser des mots ou des chaînes de caractères (mots ou non-mots). Nous montrons d’une part que la similarité de Bray-Curtis peut être plus efficace et surtout plus stable que la similarité cosinus et d’autre part que le calcul de similarité sur des chaînes de caractères est plus efficace que le même calcul sur des mots.