GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters

• URL: http://arxiv.org/abs/2304.12979v1
• Date: Tue, 25 Apr 2023 16:39:51 GMT
• Title: GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters
• Authors: Md Mahfuz Ibn Alam, Ruoyu Xie, Fahim Faisal, Antonios Anastasopoulos
• Abstract summary: This report describes GMU's sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. Our approach uses models with AfroXLMR-large, a pre-trained multilingual language model trained on African languages. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track.
• Score: 35.36372251094268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report describes GMU's sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.

Related papers

• UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis [5.945320097465418]
  We tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual models under a full supervised setting. Our results demonstrate the effectiveness of transfer learning and fine-tuning techniques for sentiment analysis.
  arXiv  Detail & Related papers  (2023-04-21T21:25:14Z)
• Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques [10.57012904999091]
  We fine-tune pre-trained multilingual transformer-based models with MIRACL dataset. Our model improvement is mainly achieved through diverse data engineering techniques. We secure 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard.
  arXiv  Detail & Related papers  (2023-02-14T12:37:32Z)
• BJTU-WeChat's Systems for the WMT22 Chat Translation Task [66.81525961469494]
  This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. Our systems achieve 0.810 and 0.946 COMET scores.
  arXiv  Detail & Related papers  (2022-11-28T02:35:04Z)
• Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task [49.916963624249355]
  This paper describes Tencent AI Lab - Shanghai Jiao Tong University (TAL-SJTU) Low-Resource Translation systems for the WMT22 shared task. We participate in the general translation task on English$Leftrightarrow$Livonian. Our system is based on M2M100 with novel techniques that adapt it to the target language pair.
  arXiv  Detail & Related papers  (2022-10-17T04:34:09Z)
• Unifying Language Learning Paradigms [96.35981503087567]
  We present a unified framework for pre-training models that are universally effective across datasets and setups. We show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
  arXiv  Detail & Related papers  (2022-05-10T19:32:20Z)
• Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task [95.06453182273027]
  This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. Our model submissions to the shared task were with DeltaLMnotefooturlhttps://aka.ms/deltalm, a generic pre-trained multilingual-decoder model. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
  arXiv  Detail & Related papers  (2021-11-03T09:16:17Z)
• The USYD-JD Speech Translation System for IWSLT 2021 [85.64797317290349]
  This paper describes the University of Sydney& JD's joint submission of the IWSLT 2021 low resource speech translation task. We trained our models with the officially provided ASR and MT datasets. To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking and transductive finetuning.
  arXiv  Detail & Related papers  (2021-07-24T09:53:34Z)
• FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings [2.362412515574206]
  In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data. We explore both monolingual and multilingual models with the standard fine-tuning method. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score.
  arXiv  Detail & Related papers  (2020-07-24T14:48:27Z)
• Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation [8.9379057739817]
  We view MT models at various training stages as human learners at different levels. We employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. We achieve 37.57 macro F1 with a 6 checkpoint model ensemble on the official English to Portuguese shared task test data.
  arXiv  Detail & Related papers  (2020-06-07T05:46:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.