@inproceedings{tran-etal-2022-jsi,
    title = "{JSI} at {S}em{E}val-2022 Task 1: {CODWOE} - Reverse Dictionary: Monolingual and cross-lingual approaches",
    author = "Tran, Thi Hong Hanh  and
      Martinc, Matej  and
      Purver, Matthew  and
      Pollak, Senja",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.12",
    doi = "10.18653/v1/2022.semeval-1.12",
    pages = "101--106",
    abstract = "The reverse dictionary task is a sequence-to-vector task in which a gloss is provided as input, and the output must be a semantically matching word vector. The reverse dictionary is useful in practical applications such as solving the tip-of-the-tongue problem, helping new language learners, etc. In this paper, we evaluate the effect of a Transformer-based model with cross-lingual zero-shot learning to improve the reverse dictionary performance. Our experiments are conducted in five languages in the CODWOE dataset, including English, French, Italian, Spanish, and Russian. Even if we did not achieve a good ranking in the CODWOE competition, we show that our work partially improves the current baseline from the organizers with a hypothesis on the impact of LSTM in monolingual, multilingual, and zero-shot learning. All the codes are available at https://github.com/honghanhh/codwoe2021.",
}