Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming

• URL: http://arxiv.org/abs/2110.12416v1
• Date: Sun, 24 Oct 2021 11:26:29 GMT
• Title: Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming
• Abstract summary: This paper presents two strategies for sentence punctuation for text sequences of game commentary, that is, punctuating sentences by two or three text sequence(s) originally punctuated by Youtube to obtain a complete sentence of commentary. We conducted comparative experiments utilizing and fine-tuning a state-of-the-art pre-trained generative language model among two strategies and the baseline to generate collaborative commentary.
• Score: 2.6330185040436347
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To solve the existing sentence punctuation problem for collaborative commentary generation in Esports live-streaming, this paper presents two strategies for sentence punctuation for text sequences of game commentary, that is, punctuating sentences by two or three text sequence(s) originally punctuated by Youtube to obtain a complete sentence of commentary. We conducted comparative experiments utilizing and fine-tuning a state-of-the-art pre-trained generative language model among two strategies and the baseline to generate collaborative commentary. Both objective evaluations by automatic metrics and subjective analyses showed that our strategy of punctuating sentences by two text sequences outperformed the baseline.

Related papers

• BoxComm: Benchmarking Category-Aware Commentary Generation and Narration Rhythm in Boxing [9.244368483134101]
  We present BoxComm, a large-scale dataset comprising 445 World Boxing Championship match videos with over 52K commentary sentences from professional broadcasts.<n>We propose a structured commentary taxonomy that categorizes each sentence into play-by-play, tactical, or contextual.<n>We introduce two novel and complementary evaluations tailored to sports commentary generation.
  arXiv  Detail & Related papers  (2026-04-06T04:56:49Z)
• Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches [69.57389826203699]
  We investigate whether in-context prompting alone can support real-time commentary generation that is both semantically relevant and well-timed.<n>We propose two prompting-based decoding strategies: 1) a fixed-interval approach, and 2) a novel dynamic interval-based decoding approach.<n> Experiments on Japanese and English datasets of racing and fighting games show that the dynamic interval-based decoding can generate commentary more closely aligned with human utterance timing and content using prompting alone.
  arXiv  Detail & Related papers  (2026-03-03T06:39:04Z)
• Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech [61.00008468914252]
  We recast paragraph segmentation as the missing structuring step and fill three gaps at the intersection of speech processing and text segmentation.<n> benchmarks focus on the underexplored speech domain, where paragraph segmentation has traditionally not been part of post-processing.<n>Second, we propose a constrained-decoding formulation that lets large language models insert paragraph breaks while preserving the original transcript.<n>Third, we show that a compact model (MiniSeg) attains state-of-the-art accuracy and, when extended hierarchically, jointly predicts chapters and paragraphs with minimal computational cost.
  arXiv  Detail & Related papers  (2025-12-30T23:29:51Z)
• Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction [28.20791917022439]
  This study focuses on a challenging yet promising task, Text-to-Sounding-Video (T2SV) generation.<n>It aims to generate a video with synchronized audio from text conditions, ensuring both modalities are aligned with text.<n>Two critical challenges still remain unaddressed: (1) a single, shared text caption where the text for video is equal to the text for audio often creates modal interference, and (2) the optimal mechanism for cross-modal feature interaction remains unclear.
  arXiv  Detail & Related papers  (2025-10-03T15:43:56Z)
• MatchTime: Towards Automatic Soccer Game Commentary Generation [52.431010585268865]
  We consider constructing an automatic soccer game commentary model to improve the audiences' viewing experience. First, observing the prevalent video-text misalignment in existing datasets, we manually annotate timestamps for 49 matches. Second, we propose a multi-modal temporal alignment pipeline to automatically correct and filter the existing dataset at scale. Third, based on our curated dataset, we train an automatic commentary generation model, named MatchVoice.
  arXiv  Detail & Related papers  (2024-06-26T17:57:25Z)
• Boosting Punctuation Restoration with Data Generation and Reinforcement Learning [70.26450819702728]
  Punctuation restoration is an important task in automatic speech recognition (ASR) The discrepancy between written punctuated texts and ASR texts limits the usability of written texts in training punctuation restoration systems for ASR texts. This paper proposes a reinforcement learning method to exploit in-topic written texts and recent advances in large pre-trained generative language models to bridge this gap.
  arXiv  Detail & Related papers  (2023-07-24T17:22:04Z)
• Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR [35.750921748001275]
  We propose a method of segmenting long-form speech by separating semantically complete sentences within the utterance. This prevents the ASR decoder from needlessly processing faraway context while also preventing it from missing relevant context within the current sentence.
  arXiv  Detail & Related papers  (2023-05-28T19:31:45Z)
• Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos [57.830865926459914]
  We propose a vision-language learning framework for untrimmed videos, which automatically detects informative events. Instead of coarse-level video-language alignments, we present two dual pretext tasks to encourage fine-grained segment-level alignments. Our framework is easily to tasks covering visually-grounded language understanding and generation.
  arXiv  Detail & Related papers  (2023-03-11T11:00:16Z)
• Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional Context for Continuous Speech Recognition [0.8670827427401333]
  We propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%.
  arXiv  Detail & Related papers  (2023-01-10T07:07:20Z)
• Revision for Concision: A Constrained Paraphrase Generation Task [0.3121997724420106]
  Revising for concision is a natural language processing task at the sentence level. Revising for concision requires algorithms to use only necessary words to rewrite a sentence. We curate and make available a benchmark parallel dataset that can depict revising for concision.
  arXiv  Detail & Related papers  (2022-10-25T18:20:54Z)
• Discourse Analysis for Evaluating Coherence in Video Paragraph Captions [99.37090317971312]
  We are exploring a novel discourse based framework to evaluate the coherence of video paragraphs. Central to our approach is the discourse representation of videos, which helps in modeling coherence of paragraphs conditioned on coherence of videos. Our experiment results have shown that the proposed framework evaluates coherence of video paragraphs significantly better than all the baseline methods.
  arXiv  Detail & Related papers  (2022-01-17T04:23:08Z)
• Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation [79.50868197788773]
  We develop a more accurate weakly-supervised solution by introducing Cross-Sentence Relations Mining. We explore two cross-sentence relational constraints: (1) trimmed ordering and (2) semantic consistency among sentences in a paragraph description of video activities. Experiments on two publicly available activity localisation datasets show the advantages of our approach over the state-of-the-art weakly supervised methods.
  arXiv  Detail & Related papers  (2021-07-23T20:04:01Z)
• Token-Level Supervised Contrastive Learning for Punctuation Restoration [7.9713449581347104]
  Punctuation is critical in understanding natural language text. Most automatic speech recognition systems do not generate punctuation. Recent work in punctuation restoration heavily utilizes pre-trained language models.
  arXiv  Detail & Related papers  (2021-07-19T18:24:33Z)
• Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence [59.51720326054546]
  We propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. Our model can generate more coherent texts than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-05-19T07:29:08Z)

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.