Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task

• URL: http://arxiv.org/abs/2407.00475v1
• Date: Sat, 29 Jun 2024 15:40:25 GMT
• Title: Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task
• Authors: Dmitry Nikolaev, Jorke Grotenhuis, Haleli Harel, Orly Goldwasser,
• Abstract summary: Ancient Egyptian (AE) writing system was characterised by widespread use of graphemic classifiers (determinatives) We implement a series of sequence-labelling neural models, which achieve promising performance despite the modest amount of training data. We discuss tokenisation and operationalisation issues arising from tackling AE texts and contrast our approach with frequency-based baselines.
• Score: 0.7237827208209208
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The complex Ancient Egyptian (AE) writing system was characterised by widespread use of graphemic classifiers (determinatives): silent (unpronounced) hieroglyphic signs clarifying the meaning or indicating the pronunciation of the host word. The study of classifiers has intensified in recent years with the launch and quick growth of the iClassifier project, a web-based platform for annotation and analysis of classifiers in ancient and modern languages. Thanks to the data contributed by the project participants, it is now possible to formulate the identification of classifiers in AE texts as an NLP task. In this paper, we make first steps towards solving this task by implementing a series of sequence-labelling neural models, which achieve promising performance despite the modest amount of training data. We discuss tokenisation and operationalisation issues arising from tackling AE texts and contrast our approach with frequency-based baselines.

Related papers

• Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition [110.8431434620642]
  We introduce the generative speech transcription error correction (GenSEC) challenge. This challenge comprises three post-ASR language modeling tasks: (i) post-ASR transcription correction, (ii) speaker tagging, and (iii) emotion recognition. We discuss insights from baseline evaluations, as well as lessons learned for designing future evaluations.
  arXiv  Detail & Related papers  (2024-09-15T16:32:49Z)
• An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation [97.3797716862478]
  Word-level AutoCompletion (WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its corresponding label. This work proposes an energy-based model for WLAC, which enables the context hidden vector to capture crucial information from the source sentence.
  arXiv  Detail & Related papers  (2024-07-29T15:07:19Z)
• CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens [49.569695524535454]
  We propose to represent speech with supervised semantic tokens, which are derived from a multilingual speech recognition model by inserting vector quantization into the encoder. Based on the tokens, we further propose a scalable zero-shot TTS synthesizer, CosyVoice, which consists of an LLM for text-to-token generation and a conditional flow matching model for token-to-speech synthesis.
  arXiv  Detail & Related papers  (2024-07-07T15:16:19Z)
• Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
  We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
  arXiv  Detail & Related papers  (2024-06-20T18:35:47Z)
• XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision [6.406111099707549]
  XAI-CLASS is a novel explanation-enhanced weakly-supervised text classification method. It incorporates word saliency prediction as an auxiliary task. XAI-CLASS outperforms other weakly-supervised text classification methods significantly.
  arXiv  Detail & Related papers  (2023-10-31T23:24:22Z)
• Extending an Event-type Ontology: Adding Verbs and Classes Using Fine-tuned LLMs Suggestions [0.0]
  We have investigated the use of advanced machine learning methods for pre-annotating data for a lexical extension task. We have examined the correlation of the automatic scores with the human annotation. While the correlation turned out to be strong, its influence on the annotation proper is modest due to its near linearity.
  arXiv  Detail & Related papers  (2023-06-03T14:57:47Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech [44.68649535280397]
  We propose a suite of benchmark tasks for Spoken Language Understanding Evaluation (SLUE) SLUE consists of limited-size labeled training sets and corresponding evaluation sets. We present the first phase of the SLUE benchmark suite, consisting of named entity recognition, sentiment analysis, and ASR on the corresponding datasets. We provide new transcriptions and annotations on subsets of the VoxCeleb and VoxPopuli datasets, evaluation metrics and results for baseline models, and an open-source toolkit to reproduce the baselines and evaluate new models.
  arXiv  Detail & Related papers  (2021-11-19T18:59:23Z)
• A Survey on Recent Advances in Sequence Labeling from Deep Learning Models [19.753741555478793]
  Sequence labeling is a fundamental research problem encompassing a variety of tasks. Deep learning has been employed for sequence labeling tasks due to its powerful capability in automatically learning complex features.
  arXiv  Detail & Related papers  (2020-11-13T02:29:50Z)
• Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education [1.215785021723604]
  The effort required to develop an educational scenario specific conversational agent is time consuming. Previous approaches to modeling annotations have relied on labeling thousands of examples and calculating inter-annotator agreement and majority votes. We propose using a multi-task weak supervision method combined with active learning to address these concerns.
  arXiv  Detail & Related papers  (2020-10-23T23:39:40Z)
• Cross-Thought for Sentence Encoder Pre-training [89.32270059777025]
  Cross-Thought is a novel approach to pre-training sequence encoder. We train a Transformer-based sequence encoder over a large set of short sequences. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders.
  arXiv  Detail & Related papers  (2020-10-07T21:02:41Z)

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.