Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs

By Cristian Danescu-Niculescu-Mizil, Lillian Lee. 2011

457 citations recorded by the source corpus.

Conversational participants tend to immediately and unconsciously adapt to each other's language styles: a speaker will even adjust the number of articles and other function words in their next utterance in response to the number in their partner's immediately preceding utterance. This striking level of coordination is thought to have arisen as a way to achieve social goals, such as gaining approval or emphasizing difference in status. But has the adaptation mechanism become so deeply embedded in the language-generation process as to become a reflex? We argue that fictional dialogs offer a way to study this question, since authors create the conversations but don't receive the social benefits (rather, the imagined characters do). Indeed, we find significant coordination across many families of function words in our large movie-script corpus. We also report suggestive preliminary findings on the effects of gender and other features; e.g., surprisingly, for articles, on average, characters adapt more to females than to males.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Language and Linguistics, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Experimental and Cognitive Psychology

In Language and Linguistics

In Natural Language Processing

Citations in the corpus

MASS: Masked Sequence to Sequence Pre-training for Language Generation (2019, methodology): “We conduct experiments on the Cornell movie dialog corpus (Danescu-Niculescu-Mizil & Lee, 2011) 10 that contains 140K conversation pairs.”
PALM: Pre-training an Autoencoding&autoregressive Language Model for Context-conditioned Generation (2020, methodology): “Following MASS, we conduct experiments on the Cornell Movie Dialog corpus 5 (Danescu-Niculescu-Mizil and Lee, 2011) that contains 140K conversation pairs, and use the training/test splits provided by the dataset.”
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog (2019, background): “[53, 52, 43, 20]), on different datasets (movie dialogs [9] and Reddit [20]).”
Ethical Challenges in Data-Driven Dialogue Systems (2017, background): “(Davidson et al. 2017) to gather bias metrics on several popular dialogue datasets: Twitter (Ritter, Cherry, and Dolan 2010), Reddit Politics (Serban et al. 2017b), the Cornell Movie Dialogue Corpus (Danescu-Niculescu-Mizil and Lee 2011), and the Ubuntu Dialogue Corpus (Lowe et al. 2015). As seen in”
Human-centric Dialog Training via Offline Reinforcement Learning (2020, methodology): “The language models were originally trained on two datasets: movie dialogs (Danescu-Niculescu-Mizil and Lee, 2011) and a dataset scraped from reddit.com/r/casual_conversation (Ghande-harioun et al., 2019).”
Visual Dialog (2016, methodology): “In particular, we compare VisDial, VQA, and Cornell Movie-Dialogs Corpus [8].”
Conversational AI: The Science Behind the Alexa Prize (2018, background): “Cornell Movie Dialogs (Danescu-Niculescu-Mizil and Lee, 2011), OpenSubtitles).”
Agnostic Federated Learning (2019, methodology): “For conversation , we used the Cornell movie dataset that contain movie dialogues Danescu-Niculescu-Mizil and Lee (2011).”
Auditing Data Provenance in Text-Generation Models (2018, methodology): “er annotation. We use the data from the en-fr pair for the machine translation task. The Cornell movie dialogs corpus (Dialogs) is a collection of fictional conversations extracted from movie scripts [7]. There are a total of 220,579 exchanges between pairs of characters engaging in at least 5 excha”
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2019, background): “Cornell Movie Danescu-Niculescu-Mizil and Lee (2011) constructed a corpus containing a collection of ﬁctional conversations from movie scripts, thus covering a large diversity of topics and emotional states.”
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems (2019, background): “A common source of data for open-domain dialog systems is movie scripts, among which the CORNELL dataset [38] is the largest and most commonly used.”
A Dataset for Document Grounded Conversations (2018, background): “Datasets based on movie scripts (Lison and Tiedemann, 2016; Danescu-Niculescu-Mizil and Lee, 2011a) contain artificial conversations.”
Steering Output Style and Topic in Neural Response Generation (2017, methodology): “They are extracted from Cornell Movie-Dialogs Corpus (Danescu-Niculescu-Mizil and Lee, 2011), and have 495 training and 54 validation sentences.”
“I’D Rather Just Go to Bed”: Understanding Indirect Answers (2020, neutral): “For a more recent and larger analysis, consider the Cornell Movie Dialog Corpus (Danescu-Niculescu-Mizil and Lee, 2011).”
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints (2018, methodology): “Instead, we leverage the Cornell Movie Dialogue Corpus (Danescu-Niculescu-Mizil and Lee, 2011) which is much smaller but contains accurate speaker annotations.”
A Survey of Deep Active Learning (2020, background): “[34, 109, 197, 199] CoNLL [184], NCBI [51], MedMentions [149], OntoNotes [167], DBLP, FZ, AG [147], Cora [228] Named entity recognition (NER) Question answering [13] CMDC [43], JabberWacky’s chatlogs9 Dialogue generation [104] Visual Genome [117], VQA [11] Visual question answer (VQA)”