| Challenge: | Analor is a semi-automatic tool for speech segmentation in periods but it only takes into account prosodic characteristics of speech. |
| Approach: | They propose to use a Fribourg model of macro-syntax to detect periods in syntactic and prosodic terms to develop an automatic tool for automatic segmentation of linguistic units. |
| Outcome: | The proposed tool is compared with an existing tool Analor which divides speech into smaller segments and that CRF models detect larger segments rather than macro-syntactic periods. |
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| Challenge: | Existing parsers struggle to parse spoken dialogue because of disfluencies and unmarked boundaries between sentence-like units (SUs). |
| Approach: | They hypothesize that prosody affects a parser that receives an entire dialogue turn as input, instead of gold standard pre-segmented SUs. |
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Developing Resources for Automated Speech Processing of Quebec French (2020.lrec-1)
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| Challenge: | acoustic models for automatic segmentation of Quebec French are not available for all languages . linguistic resources are developed to perform phonetic annotations in Quebec French . physical characteristics of speech can be observed in the production of sounds . |
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A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks (L18-1)
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| Challenge: | Phonetic segmentation is the process of splitting speech into distinct phonetic units . methods for automatic segmentation are not always accurate enough . |
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Subword Segmentation in LLMs: Looking at Inflection and Consistency (2024.emnlp-main)
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| Challenge: | Subword segmentation is not linguistically guided and is not currently well understood in LLMs. |
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The MonPaGe_HA Database for the Documentation of Spoken French Throughout Adulthood (L18-1)
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An Automatic Tool For Language Evaluation (2020.lrec-1)
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| Challenge: | standardized tests are used to assess and screen developmental language impairments but require manual laborious transcription, annotation and calculation. |
| Approach: | They propose to use the correct sentence and the sentence produced by patients to evaluate the level of verbal production and return a score. |
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Joint Learning of Syntactic Features Helps Discourse Segmentation (2020.lrec-1)
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| Challenge: | Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs). |
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Improving Text Readability through Segmentation into Rheses (2024.lrec-main)
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| Challenge: | a new study examines the segmentation of sentences into rheses to improve readability for dyslexics . short lines of text can be beneficial for dyslexia sufferers as it limits attention span . however, random line splits can be confusing than helpful . |
| Approach: | They propose to segment sentences into rhythmic and semantic units to improve comprehension . they also use a bilingual dataset to evaluate the efficiency of their approach . |
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Segmenting Natural Language Sentences via Lexical Unit Analysis (2021.findings-emnlp)
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| Challenge: | Recent work on sequence segmentation models suffer from invalid predictions and a lack of consistency. |
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Visualizing the “Dictionary of Regionalisms of France” (DRF) (L18-1)
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| Challenge: | a corpus of regionalisms, parts of speech and recognition rates is published in the Dictionnaire des Régionalismes de France. |
| Approach: | They propose to curate and analyze the corpus of regionalisms published in the Dictionnaire des Régionalismes de France. |
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