HiTZ/Multilingual-Opinion-Target-Extraction

This repository contains the English 'SemEval-2014 Task 4: Aspect Based Sentiment Analysis'. translated with DeepL into Spanish, French, Russian, and Turkish. The labels have been manually projected. For more details, read this paper: Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings.

Intended Usage: Since the datasets are parallel across languages, they are ideal for evaluating annotation projection algorithms, such as T-Projection.

Label Dictionary

{
"O": 0,
"B-TARGET": 1,
"I-TARGET": 2
}

Cication

If you use this data, please cite the following papers:

@inproceedings{garcia-ferrero-etal-2022-model,
    title = "Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings",
    author = "Garc{\'\i}a-Ferrero, Iker  and
      Agerri, Rodrigo  and
      Rigau, German",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.478",
    doi = "10.18653/v1/2022.findings-emnlp.478",
    pages = "6403--6416",
    abstract = "Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.",
}

@inproceedings{pontiki-etal-2014-semeval,
    title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
    author = "Pontiki, Maria  and
      Galanis, Dimitris  and
      Pavlopoulos, John  and
      Papageorgiou, Harris  and
      Androutsopoulos, Ion  and
      Manandhar, Suresh",
    editor = "Nakov, Preslav  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
    month = aug,
    year = "2014",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S14-2004",
    doi = "10.3115/v1/S14-2004",
    pages = "27--35",
}