TVShowGuess: Character Comprehension in Stories as Speaker Guessing

Sang, Yisi; Mou, Xiangyang; Yu, Mei; Yao, Shunyu; Li, Jing; Stanton, Jeffrey M.

doi:10.18653/v1/2022.naacl-main.317

Cited by 6 publications

(16 citation statements)

References 34 publications

(66 reference statements)

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“…), and recently, coreference resolution (Choi and Chen, 2018). Despite the level of formality and breadth of domains across all datasets, the domain of each multiparty dataset itself is usually narrowly focused, like meetings (McCowan et al, 2005;Hsueh et al, 2006), board game play (Asher et al, 2016), fantasy storytelling (Rameshkumar and Bailey, 2020), technical or persuasive online forums (Li et al, 2020a;Wang et al, 2019) and sitcom transcripts (Choi and Chen, 2018;Sang et al, 2022).…”

Section: Multiparty Conversationsmentioning

confidence: 99%

See 1 more Smart Citation

Multilingual Coreference Resolution in Multiparty Dialogue

Zheng¹,

Xia²,

Yarmohammadi³

et al. 2022

Preprint

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Existing multiparty dialogue datasets for coreference resolution are nascent, and many challenges are still unaddressed. We create a largescale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.

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Section: Multiparty Conversationsmentioning

confidence: 99%

“…they contain rich multiparty dialogues and multiple references to the interlocutors. We select Friends and The Big Bang Theory (TBBT) because there is prior work in preprocessing and speaker identification for the transcripts of these shows (Roy et al, 2014;Choi and Chen, 2018;Sang et al, 2022).…”

Section: Parallel Dialogue Corpusmentioning

confidence: 99%

Multilingual Coreference Resolution in Multiparty Dialogue

Zheng¹,

Xia²,

Yarmohammadi³

et al. 2022

Preprint

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show abstract

“…narrative stories Sang et al 2022). It becomes even more challenging for cross-domain inference, where the system neither has prior knowledge about candidate speakers, nor it could identify the absent speaker mentioned in context for those implicit cases.…”

Section: Introductionmentioning

confidence: 99%

SIG: Speaker Identification in Literature via Prompt-Based Generation

Su,

Xu,

et al. 2024

AAAI

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Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context. In this work, we propose a simple and effective approach SIG, a generation-based method that verbalizes the task and quotation input based on designed prompt templates, which also enables easy integration of other auxiliary tasks that further bolster the speaker identification performance. The prediction can either come from direct generation by the model, or be determined by the highest generation probability of each speaker candidate. Based on our approach design, SIG supports out-of-domain evaluation, and achieves open-world classification paradigm that is able to accept any forms of candidate input. We perform both cross-domain evaluation and in-domain evaluation on PDNC, the largest dataset of this task, where empirical results suggest that SIG outperforms previous baselines of complicated designs, as well as the zero-shot ChatGPT, especially excelling at those hard non-explicit scenarios by up to 17% improvement. Additional experiments on another dataset WP further corroborate the efficacy of SIG.

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“…According to the data modality and format, character comprehension can be categorized into several classes (Sang et al, 2022a). In this work, we focus on character understanding in scripts (Chen and Choi, 2016;Sang et al, 2022b). Scripts are written text for plays, movies, or broadcasts (Onions et al, 1966).…”

Section: Introductionmentioning

confidence: 99%

“…Comparison between a script from TVSHOWGUESS (Sang et al, 2022b) and a narrative from ROCStories (Mostafazadeh et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Multi-level Contrastive Learning for Script-based Character Understanding

Li,

Zhang,

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances. We begin by analyzing several challenges in this scenario, and then propose a multi-level contrastive learning framework to capture characters' global information in a fine-grained manner. To validate the proposed framework, we conduct extensive experiments on three character understanding sub-tasks by comparing with strong pretrained language models, including SpanBERT, Longformer, BigBird and ChatGPT-3.5. Experimental results demonstrate that our method improves the performances by a considerable margin. Through further in-depth analysis, we show the effectiveness of our method in addressing the challenges and provide more hints on the scenario of character understanding. We will open-source our work in this URL. Character Sheldon Jennifer Story Title TBBT The Test Dataset TVSHOWGUESS ROCStories Text Length 528832 41 Character's Related Text Sheldon: " ... we take on Koothrappaliand his dog. Really give ourselves a challenge." Jennifer has a big exam tomorrow. ... Jennifer felt bittersweet about it.

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TVShowGuess: Character Comprehension in Stories as Speaker Guessing

Cited by 6 publications

References 34 publications

Multilingual Coreference Resolution in Multiparty Dialogue

Multilingual Coreference Resolution in Multiparty Dialogue

SIG: Speaker Identification in Literature via Prompt-Based Generation

Multi-level Contrastive Learning for Script-based Character Understanding

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