Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation

• URL: http://arxiv.org/abs/2406.03881v1
• Date: Thu, 6 Jun 2024 09:18:42 GMT
• Title: Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation
• Authors: Matthias Sperber, Ondřej Bojar, Barry Haddow, Dávid Javorský, Xutai Ma, Matteo Negri, Jan Niehues, Peter Polák, Elizabeth Salesky, Katsuhito Sudoh, Marco Turchi,
• Abstract summary: We conduct a comprehensive human evaluation of the results of several shared tasks from the last International Workshop on Spoken Language Translation (IWSLT 2023) We propose an effective evaluation strategy based on automatic resegmentation and direct assessment with segment context. Our analysis revealed that: 1) the proposed evaluation strategy is robust and scores well-correlated with other types of human judgements; 2) automatic metrics are usually, but not always, well-correlated with direct assessment scores; and 3) COMET as a slightly stronger automatic metric than chrF.
• Score: 50.60733773088296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human evaluation is a critical component in machine translation system development and has received much attention in text translation research. However, little prior work exists on the topic of human evaluation for speech translation, which adds additional challenges such as noisy data and segmentation mismatches. We take first steps to fill this gap by conducting a comprehensive human evaluation of the results of several shared tasks from the last International Workshop on Spoken Language Translation (IWSLT 2023). We propose an effective evaluation strategy based on automatic resegmentation and direct assessment with segment context. Our analysis revealed that: 1) the proposed evaluation strategy is robust and scores well-correlated with other types of human judgements; 2) automatic metrics are usually, but not always, well-correlated with direct assessment scores; and 3) COMET as a slightly stronger automatic metric than chrF, despite the segmentation noise introduced by the resegmentation step systems. We release the collected human-annotated data in order to encourage further investigation.

Related papers

• CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization [7.234196390284036]
  This article summarizes the research on Transformer-based abstractive summarization for English dialogues. We cover the main challenges present in dialog summarization (i.e., language, structure, comprehension, speaker, salience, and factuality) We find that while some challenges, like language, have seen considerable progress, others, such as comprehension, factuality, and salience, remain difficult and hold significant research opportunities.
  arXiv  Detail & Related papers  (2024-06-11T17:30:22Z)
• DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering [95.89707479748161]
  Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. We propose a metric called DecompEval that formulates NLG evaluation as an instruction-style question answering task. We decompose our devised instruction-style question about the quality of generated texts into the subquestions that measure the quality of each sentence. The subquestions with their answers generated by PLMs are then recomposed as evidence to obtain the evaluation result.
  arXiv  Detail & Related papers  (2023-07-13T16:16:51Z)
• Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks [45.550554287918885]
  This paper focuses on evaluating the usefulness of text summaries with extrinsic methods. We design three different downstream tasks for extrinsic human evaluation of summaries, i.e., question answering, text classification and text similarity assessment. We find summaries are particularly useful in tasks that rely on an overall judgment of the text, while being less effective for question answering tasks.
  arXiv  Detail & Related papers  (2023-05-24T11:34:39Z)
• SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation [52.186343500576214]
  We introduce SEAHORSE, a dataset for multilingual, multifaceted summarization evaluation. SEAHORSE consists of 96K summaries with human ratings along 6 dimensions of text quality. We show that metrics trained with SEAHORSE achieve strong performance on the out-of-domain meta-evaluation benchmarks TRUE and mFACE.
  arXiv  Detail & Related papers  (2023-05-22T16:25:07Z)
• Large Language Models are Diverse Role-Players for Summarization Evaluation [82.31575622685902]
  A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. We propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects.
  arXiv  Detail & Related papers  (2023-03-27T10:40:59Z)
• Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation [160.07938471250048]
  Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. We develop strong-performing automatic metrics for reference-based summarization evaluation.
  arXiv  Detail & Related papers  (2023-03-07T02:49:50Z)
• Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation [136.16507050034755]
  Existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient scale. We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which is based on fine-grained semantic units. We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of 22,000 summary-level annotations over 28 top-performing systems.
  arXiv  Detail & Related papers  (2022-12-15T17:26:05Z)
• Towards Interpretable Summary Evaluation via Allocation of Contextual Embeddings to Reference Text Topics [1.5749416770494706]
  The multifaceted interpretable summary evaluation method (MISEM) is based on allocation of a summary's contextual token embeddings to semantic topics identified in the reference text. MISEM achieves a promising.404 Pearson correlation with human judgment on the TAC'08 dataset.
  arXiv  Detail & Related papers  (2022-10-25T17:09:08Z)
• Does Summary Evaluation Survive Translation to Other Languages? [0.0]
  We translate an existing English summarization dataset, SummEval dataset, to four different languages. We analyze the scores from the automatic evaluation metrics in translated languages, as well as their correlation with human annotations in the source language.
  arXiv  Detail & Related papers  (2021-09-16T17:35:01Z)
• Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation [19.116396693370422]
  We propose an evaluation methodology grounded in explicit error analysis, based on the Multidimensional Quality Metrics framework. We carry out the largest MQM research study to date, scoring the outputs of top systems from the WMT 2020 shared task in two language pairs. We analyze the resulting data extensively, finding among other results a substantially different ranking of evaluated systems from the one established by the WMT crowd workers.
  arXiv  Detail & Related papers  (2021-04-29T16:42:09Z)

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.