Learning From Personal Longitudinal Dialog Data

Welch, Charles; Pérez-Rosas, Verónica; Kummerfeld, Jonathan K.; Mihalcea, Rada; Cambria, Erik

doi:10.1109/mis.2019.2916965

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Personalization has been studied for marketing, webpage layout, recommendations, query completion, and dialog (Eirinaki and Vazirgiannis, 2003;Das et al, 2007). Our prior work (Welch et al, 2019a;Welch et al, 2019b) explored predicting response time, common messages, and author relationships from personal conversation data. Zhang et al (2018) conditioned dialog systems on artificially constructed personas and Madotto et al (2019) used meta-learning to improve this process.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Value of Personalized Word Embeddings

Welch

Kummerfeld

Pérez-Rosas

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In this paper, we introduce personalized word embeddings, and examine their value for language modeling. We compare the performance of our proposed prediction model when using personalized versus generic word representations, and study how these representations can be leveraged for improved performance. We provide insight into what types of words can be more accurately predicted when building personalized models. Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4.7% relative reduction in perplexity. Additionally, we show that a language model using personalized word embeddings can be effectively used for authorship attribution.

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Section: Related Workmentioning

confidence: 99%

Exploring the Value of Personalized Word Embeddings

Welch

Kummerfeld

Pérez-Rosas

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…It also comes in different flavors depending on granularity of the analysis [210], modality adopted [211] and gender [212,213]. Applications of sentiment analysis span domains like healthcare [214,215], political forecasting [216], tourism [217], rumors and fake news detection [218,219] and dialogue systems [220].…”

Section: Sentiment Analysismentioning

confidence: 99%

The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

Camacho¹,

Panizo-LLedot²,

Bello-Orgaz³

et al. 2020

Preprint

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Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA); (2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area.

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“…As a result, research on multi-session dialogues resorts to crowd-sourcing datasets with superficial persona statements and pretended longitudinality (Xu et al, 2022a,b;Bae et al, 2022). Meanwhile, studies on LDs have been limited to inferring user's attributes such as age and gender (Welch et al, 2019b), or next quickresponse selection from a candidate set of "yes," "haha," "okay," "oh," and "nice" (Welch et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?

Mousavi,

Caldarella,

Riccardi

2023

Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)

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Longitudinal Dialogues (LD) are the most challenging type of conversation for humanmachine dialogue systems. LDs include the recollections of events, personal thoughts, and emotions specific to each individual in a sparse sequence of dialogue sessions. Dialogue systems designed for LDs should uniquely interact with the users over multiple sessions and long periods of time (e.g. weeks), and engage them in personal dialogues to elaborate on their feelings, thoughts, and real-life events. In this paper, we study the task of response generation in LDs. We evaluate whether general-purpose Pre-trained Language Models (PLM) are appropriate for this purpose. We fine-tune two PLMs, GePpeTto (GPT-2) and iT5, using a dataset of LDs. We experiment with different representations of the personal knowledge extracted from LDs for grounded response generation, including the graph representation of the mentioned events and participants. We evaluate the performance of the models via automatic metrics and the contribution of the knowledge via the Integrated Gradients technique. We categorize the natural language generation errors via human evaluations of contextualization, appropriateness and engagement of the user.

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Learning From Personal Longitudinal Dialog Data

Cited by 11 publications

References 15 publications

Exploring the Value of Personalized Word Embeddings

Exploring the Value of Personalized Word Embeddings

The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?

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