TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection

• URL: http://arxiv.org/abs/2405.17129v2
• Date: Tue, 2 Jul 2024 12:18:51 GMT
• Title: TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection
• Authors: Long Cheng, Qihao Shao, Christine Zhao, Sheng Bi, Gina-Anne Levow,
• Abstract summary: Cross-lingual emotion detection allows us to analyze global trends, public opinion, and social phenomena at scale. Our system outperformed the baseline by more than 0.16 F1-score absolute, and ranked second amongst competing systems.
• Score: 5.942385193284472
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cross-lingual emotion detection allows us to analyze global trends, public opinion, and social phenomena at scale. We participated in the Explainability of Cross-lingual Emotion Detection (EXALT) shared task, achieving an F1-score of 0.6046 on the evaluation set for the emotion detection sub-task. Our system outperformed the baseline by more than 0.16 F1-score absolute, and ranked second amongst competing systems. We conducted experiments using fine-tuning, zero-shot learning, and few-shot learning for Large Language Model (LLM)-based models as well as embedding-based BiLSTM and KNN for non-LLM-based techniques. Additionally, we introduced two novel methods: the Multi-Iteration Agentic Workflow and the Multi-Binary-Classifier Agentic Workflow. We found that LLM-based approaches provided good performance on multilingual emotion detection. Furthermore, ensembles combining all our experimented models yielded higher F1-scores than any single approach alone.

Related papers

• Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following [51.18383180774354]
  We introduce Multi-IF, a new benchmark designed to assess Large Language Models' proficiency in following multi-turn and multilingual instructions. Our evaluation of 14 state-of-the-art LLMs on Multi-IF reveals that it presents a significantly more challenging task than existing benchmarks. languages with non-Latin scripts (Hindi, Russian, and Chinese) generally exhibit higher error rates, suggesting potential limitations in the models' multilingual capabilities.
  arXiv  Detail & Related papers  (2024-10-21T00:59:47Z)
• Transformer based neural networks for emotion recognition in conversations [4.915541242112533]
  Paper outlines approach of the ISDS-NLP team in SemEval 2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF).
  arXiv  Detail & Related papers  (2024-05-18T08:05:05Z)
• Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models [73.11488464916668]
  This study investigates the dynamics of the multilingual pretraining process. We probe checkpoints taken from throughout XLM-R pretraining, using a suite of linguistic tasks. Our analysis shows that the model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones.
  arXiv  Detail & Related papers  (2022-05-24T03:35:00Z)
• Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling [101.74165219364264]
  Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages. In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap. Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel
  arXiv  Detail & Related papers  (2022-04-11T15:55:20Z)
• X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering [55.57776147848929]
  We propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for Natural Language Understanding (NLU) Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages.
  arXiv  Detail & Related papers  (2021-04-20T00:13:35Z)
• Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings [41.148892848434585]
  We propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly. We then propose a sense alignment objective on top of the sense-aware cross entropy loss for cross-lingual model pretraining, and pretrain cross-lingual models for several language pairs.
  arXiv  Detail & Related papers  (2021-03-11T04:55:35Z)
• Making Pre-trained Language Models Better Few-shot Learners [11.90626040104822]
  Recent GPT-3 model achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples.
  arXiv  Detail & Related papers  (2020-12-31T17:21:26Z)
• Mixed-Lingual Pre-training for Cross-lingual Summarization [54.4823498438831]
  Cross-lingual Summarization aims at producing a summary in the target language for an article in the source language. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks like translation and monolingual tasks like masked language models. Our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.
  arXiv  Detail & Related papers  (2020-10-18T00:21:53Z)
• Explicit Alignment Objectives for Multilingual Bidirectional Encoders [111.65322283420805]
  We present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bi-directional EncodeR) AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model.
  arXiv  Detail & Related papers  (2020-10-15T18:34:13Z)
• On Learning Universal Representations Across Languages [37.555675157198145]
  We extend existing approaches to learn sentence-level representations and show the effectiveness on cross-lingual understanding and generation. Specifically, we propose a Hierarchical Contrastive Learning (HiCTL) method to learn universal representations for parallel sentences distributed in one or multiple languages. We conduct evaluations on two challenging cross-lingual tasks, XTREME and machine translation.
  arXiv  Detail & Related papers  (2020-07-31T10:58:39Z)

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.