MMG at S em E val-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective (2022)

Abstract: This paper presents a novel and linguistic-driven system for the Spanish Reverse Dictionary task of SemEval-2022 Task 1. The aim of this task is the automatic generation of a word using its gloss. The conclusion is that this task results could improve if the quality of the dataset did as well by incorporating high-quality lexicographic data. Therefore, in this paper we analyze the main gaps in the proposed dataset and describe how these limitations could be tackled.

## introduction the codwoe (comparison of word glosses and word embeddings) task at semeval-2022 @xcite encouraged participants to analyze the relation between two types of semantic descriptions, word embeddings and dictionary glosses, by proposing two sub-tasks: reverse dictionary (rd) @xcite , in which participants must generate vectors from glosses, and definition modeling (dm) @xcite , in which participants must generate glosses from vectors. these subtasks aim to be useful for explainable artificial intelligence (ai) by including human-readable and machine-readable data. given the didactic nature of these tasks, the output generated by these models should be as accurate as the most prestigious dictionaries. hence, the process of selecting a quality dataset is a critical phase, as @xcite state: "a small number of data examples prevents an effective convergence to the task, while noisy data leads to incorrect convergence". in this case, tasks require that the glosses used in the training represent the exact meaning of the word being defined in the context that the embeddings were extracted. however, as per our understanding, coherence, rigour and lexicographical prestige of the provided dataset should be improved; although accessing a prestigious dictionary is not an easy task. a reverse dictionary takes a description in natural language and generates a list of words satisfying it @xcite . first rd were information retrieval systems for turkish @xcite and japanese @xcite . other approaches used lexical graphs @xcite that capture the relationships between the words of the definition itself and between these and others similar to them at different levels (synonymy, hyperonymy, etc.). other systems create a vector space from these lexical resources, such as wordnet @xcite ; méndez id: definition es.train.212: "biología.-se dice de los microorganismos que no aceptan los colorantes habituales." es.train.250: "zoología.-cualquiera de los colibríes del género chlorostilbon ." es.train.119: "servirse , darse ayuda mutuamente ." es.train.120: "trabajar ( uso pronominal de ... )" @xcite or transformers @xcite . language models (lms) based on recurrent neural networks (rnns) @xcite have also been used. finally, from a linguistic point of view, @xcite add syntactic knowledge, @xcite try to replicate the model of the mental dictionary and @xcite use morphological knowledge in their system. definition modeling is a relatively new task based on using distributed word representations to generate its definition. @xcite use an rnn to compute the probability of a word being part of the definition. different approaches have been proposed to this natural language generation (nlg) task. usually these new methods are heavily focused on the importance of the context of the word being defined, like fine-tuning a bart model to define groups of words @xcite , using attention and a skip-gram model to smooth the problems of word selection in the generation step @xcite , or exploring new ways to understand the embeddings and their capabilities, resulting these in a new task named "usage modeling" @xcite . there have been few attempts to use pure linguistics traits to improve definition generation, like accounting polysemy as a generative target using multi-sense word embeddings @xcite or using sememes to condense the semantic core of generated sentences @xcite . this paper has the following structure. chapter 2 contains a review of the data along with some linguistic knowledge we consider relevant. in chapter 3 the rd approach and results are presented. finally, chapter 4 contains the conclusions and future work. our contributions are the following: ## data analysis this section contains a review of the spanish dataset structure, an introduction about relevant lexicography concepts and the rd task preprocessing techniques. ## conclusions for these tasks a combination of machine learning techniques and linguistic knowledge was proposed, in order to achieve good results and to understand the problems and the future challenges of these tasks. in this paper, the main gaps in the dataset from a lexicographic perspective and its lack of coherence and exactitude were explained, and then a preprocessing solution was proposed, which was finally used in the rd system to avoid the problems that the dataset could carry in the model. eventually, mmg team has presented a novel approach with an architecture that is easily scalable to other languages and embedding types. we understand that due to the use of external resources our results in the task are not valid for the challenge. however, we would like to remark that the lexical graph approach, which in the end we did not submit due to speed issues, achieved almost similar results. these tasks are considered to represent an excellent starting point for research on the relationships between dictionaries and word embeddings. both subtasks in general and our research on them in particular open up many options for further investigation. in our case, our intention is to use more linguistic knowledge at different levels, further exploring the power of linguistic graphs and putting into practice what we have learned in the reverse dictionary task to create quality definition modeling systems.