Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

Fu, Liye; Chang, Jonathan P.; Danescu-Niculescu-Mizil, Cristian

doi:10.48550/arxiv.1904.01587

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…We use a combination of 17 datasets for our largestscale training data retrieval. The datasets include αNLI , SWAG (Zellers et al, 2018), RACE (Lai et al, 2017) (we only use the middle-school subset), CODAH ), RiddleSense (Lin et al, 2021, SciTail , Com2Sense (Singh et al, 2021), AI2 Science Questions (Clark et al, 2019), Wino-Grade , CommonsenseQA (Talmor et al, 2019), CommonsenseQA2.0 (Talmor et al, 2021, ASQ (Fu et al, 2019), OBQA (Mihaylov et al, 2018), PhysicalIQA (Bisk et al, 2020), SocialIQA (Sap et al, 2019b), CosmosQA (Huang et al, 2019) and HellaSWAG (Zellers et al, 2019). We present details of the datasets that we use for training data retrieval in Table 6.…”

Section: A Datasetsmentioning

confidence: 99%

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Yi‐chong¹,

C²,

Wang³

et al. 2021

Preprint

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Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications.In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities. The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.

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Section: A Datasetsmentioning

confidence: 99%

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Yi‐chong¹,

C²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Our work is preceded by substantial efforts toward document (Blei et al, 2003;Dai et al, 2015;Yang et al, 2016) and story (Mostafazadeh et al, 2016;Chaturvedi et al, 2018;Iyyer et al, 2016;Antoniak et al, 2019;Fu et al, 2019) representation. We find that most approaches to text similarity focus on non-narrative corpora (vor der Brück and Pouly, 2019;Lin et al, 2013;Cer et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types

Saldias¹,

Roy²

2020

Preprint

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Sharing personal narratives is a fundamental aspect of human social behavior as it helps share our life experiences. We can tell stories and rely on our background to understand their context, similarities, and differences. A substantial effort has been made towards developing storytelling machines or inferring characters' features. However, we don't usually find models that compare narratives. This task is remarkably challenging for machines since they, as sometimes we do, lack an understanding of what similarity means. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives (i.e., action, orientation, and evaluation clause types) and train a classifier that reaches 84.7% F-score for the highest-agreed clauses. Finally, we match stories and explore whether people implicitly rely on Labov's framework to compare narratives. We show that actions followed by the narrator's evaluation of these are the aspects non-experts consider the most. Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.

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“…People asking questions on CQA sites sometimes first offer some personal information as context. Such self-disclosure can be necessary because it enables posters to introduce their situations and helps others better understand their requests (Fu, Chang, and Danescu-Niculescu-Mizil 2019).…”

Section: Introductionmentioning

confidence: 99%

“I Am 30F and Need Advice!”: A Mixed-Method Analysis of the Effects of Advice-Seekers’ Self-Disclosure on Received Replies

Chen,

Hale,

Hogan

2024

ICWSM

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In community question answering sites, users can easily make a post to ask questions or seek advice. Others volunteer replies to these posts to provide answers of varying quality, detail, and helpfulness. In the advice-seeking process, self-disclosure enables posters to provide a relatable context for their requests but comes at a cost of greater identifiability. We focus on the "r/Advice" Reddit community and present a mixed-method study on how self-disclosure of advice-seekers shapes the prevalence and detail of the feedback received. We focus particularly on age and gender disclosure as both are reliably detected and normatively considered in the context of giving advice. We use both hurdle negative binomial regression models and discourse analysis to examine the relationship between self-disclosure and the replies received and explore themes related to disclosure. The results show that advice-seekers' age or gender disclosure correlates with more replies and more helpful replies, but the effects of age and gender disclosure are not additive. We also find both reciprocity and homophily effects in disclosure as reply-givers are more likely to self-disclose when the advice-seeker does so. The lack of additive effects alongside the thematic analysis suggests disclosure practices are used to elicit sufficient credibility or basis for empathy, whereas too much or too little disclosure creates uncertainty or inhibits the applicability of the received advice.

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Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

Cited by 5 publications

References 35 publications

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types

“I Am 30F and Need Advice!”: A Mixed-Method Analysis of the Effects of Advice-Seekers’ Self-Disclosure on Received Replies

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