Grounding Conversations with Improvised Dialogues (2020)

Abstract: Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people. Modern dialogue systems are not explicitly trained to build common ground, and therefore overlook this important aspect of communication. Improvisational theater (improv) intrinsically contains a high proportion of dialogue focused on building common ground, and makes use of the yes-and principle, a strong grounding speech act, to establish coherence and an actionable objective reality. We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier. We fine-tune chit-chat dialogue systems with our corpus to encourage more grounded, relevant conversation and confirm these findings with human evaluations.

## introduction for humans, dialogue is fundamentally a collaborative, cooperative process by which partners coordinate via turns or acts to jointly construct a common world state @xcite . without coordination, partners may establish different or conflicting world states, leading to solipsism in the best case and conflict in the worst. @xcite , describe five dimensions of grounding, by which partners cooperate to establish common ground, or a shared world state. the dimension of "initiation of next relevant contribution" is the most effective of these in expressing understanding of an ongoing dialogue, and yet is the least observed in dialogue systems. improvisational theater (improv) is a form of theater in which most or all of what is performed is unscripted, created spontaneously by the actors in real time. because the performance is not scripted and there is typically little to no scenery or other established environment, foot_0 there is no objective reality that can naturally ground the scene. hence, actors must mainly rely on dialogue in order to build a coherent scene and progressively establish a common world view. this necessitates accelerated use of the "initiation of next relevant contribution," which in improv is known as the yes-and principle. the yes-and principle is a rule-of-thumb that suggests that a participant should accept the reality of what the other participant has said ("yes") and expand or refine that reality with additional information ("and"). since actors consciously abide by this principle during improv performances, there is a high proportion of these turns embedded in improv dialogue, which helps ensure scenes are coherent and interesting. open-domain neural dialogue systems, by contrast, specifically lack coherence and interestingness. they commonly repeat previous utterances @xcite or generate non-committal, generic statements such as i don't know that are logically coherent as a response but preempt further conversation @xcite @xcite . either of these developments leads to a conversational black hole and discourages participation in further dialogue turns. this is a critical shortcoming for open-domain dialogue agents, which, unlike task-oriented dialogue systems, are not guided by specific objectives other than entertainment @xcite . it would behoove such systems to adopt the strategies improvisers include by habit in their dialogues and, consequently, incorporating improv acts should be a key focus for the dialogue community. yet, to the best of our knowledge, this has not been previously done. there has been work in applying improv to build believable agents that interact with humans @xcite or generate improvised stories @xcite , but development of improvcapable systems in the neural era is largely absent, stymied, we suspect, by the lack of substantial corpora. this is unsurprising; while improv speech acts such as yes-and are crucial in all dialogues, they are only highly concentrated in improv dialogues. and improv dialogues are quite difficult to collect; research collections @xcite have been far too small to be useful in the modern ml era. the art form has historically been mostly ephemeral, performed live in regional venues on shoestring budgets and rarely recorded. 2 transcripts are all but absent and mainstream media products are rare. 3 however, the liberalization of high quality audio podcasts since 2014 has enabled the availability of a long tail of niche products, improv included (mchugh, 2016). 2 the art form has long roots, extending to the italian commedia dell'arte tradition from the 16th century and farces from the roman era, but we constrain our focus to the post-20th century form developed and championed by e.g. keith johnstone @xcite , del close @xcite , and our corpus' namesake, viola spolin @xcite . spolin was the originator of theater games, acting exercises that encourage the development of specific theatrical skills. as our corpus is similarly designed to elicit specific skills, we backronym it in recognition of her influence. therefore we set our objective as collecting yesand-type dialogue pairs (yes-ands) to enable their modeling by corpus-driven dialogue systems. we mine podcasts and existing movie script corpora for dialogue that abides by the yes-and principle and extract dialogue pairs from these sources to build the selected pairs of learnable improvisa-tion (spolin) corpus. spolin is a collection of more than 26,000 english dialogue turn pairs, each consisting of a prompt and subsequent response, which abide by the yes-and principle, though in diverse manners. examples of yes-and type dialogue pairs collected for spolin are in figure 1 . the corpus is substantial enough to be usable for fine-tuning existing dialogue models to encourage more yes-and behavior, and beyond that may prove a valuable knowledge base for empirical sociolinguistic studies on this dialogue act. our contributions are summarized as follows: ## data collection our data collection has five stages: an overview of this process is shown in figure 2 . ## dataset analysis in order to provide a better understanding on the characteristics of our corpus, we annotate 200 yesands and 200 non-yes-ands in spolin's development set to categorize them into specific yes-and or non-yes-and types. we classify yes-ands into explicit yes-ands, implicit yes-ands, or yes-buts. only 15% of all yesands are explicit yes-ands, containing phrases such as "yeah" or "sure" that reflects agreement. even with such phrases, identifying explicit yes-ands is not a trivial task because it requires semantic understanding of the relevance of the context established in the prompt and that introduced in the response. in fact, there are non-yes-ands that contain phrases affirming agreement but have no contributions or have new contributions that lack relevance. the majority (78%) of yes-ands are implicit yes-ands, meaning that the agreement is implied, often in a subtle manner. the remaining 7% are yes-buts. non-yes-ands are divided into contradictions and others. most of the non-yes-and were others, as only 5% of candidates extracted from cornell are contradictions, which are dialogue pairs with a response that actively negates the reality in the prompt. others encompass any dialogue pairs with a response that lacks coherence to the prompt or adds no or minimal contributions. the distribution and examples of different types of yes-ands and non-yes-ands are shown in the main focus of our work is on yes-ands, but we provide non-yes-ands as part of spolin for those interested in training their own classifiers. the negative samples are collected using the methods described in section 2.2. the composition details of spolin are shown in ## experiments to evaluate the effect of spolin on generating yes-and responses and thus improving generated dialogue quality, we train a common architecture with a variety of fine-tuning data configurations, both with and without spolin. specifically, for each data configuration we fine-tune a doublehead gpt-2 model (117m-parameter version based on the implementation by @xcite ), which achieved state-of-the-art performance on personachat for the convai-2 dialogue competition @xcite . we fine-tune the models using two learning objectives, which we weigh equally in calculating loss: the language modeling component uses pretrained weights from openai, while the candidate classification head is trained from scratch. for evaluation, we use the language modeling component of the fine-tuned model to generate single-turn responses for the yes-and prompts in the development set. we use nucleus sampling @xcite for the decoding step to keep only the top tokens with a cumulative probability that together exceed 0.9, from which the next token is chosen with multinomial sampling. ## extracting from other corpora the approach to classifier-based mining we describe in section 2.2 can naturally be applied to other dialogue corpora. we thus next consider mining the gigantic (441m sentence) opensubtitles @xcite collection. as opensubtitles contains undesirable material, such as subtitles for media with minimal dialogue, we instead mine from the (3.3m sentence) subtle corpus @xcite , a preprocessed subset of opensubtitles that heuristically combines subtitle sequences into dialogue form. by iterating through half of this corpus, we collect more than 40,000 yes-ands from it alone, which, when added to spolin, yields what we call spolin-extended, which contains about 68,000 yes-ands, more than 2.5 times the size of the core spolin. heuristics for finding alternations mean that subtle's utterances are shorter than those in spontaneanation and cornell, so once the proportion of utterances longer than the average length of in spontaneanation and cornell (18.5 words) is less than 40%, we stop further collection in the remainder of the dataset. spolinextended is available in the same public repository as spolin. details of the iterative process as applied to subtle are in the appendix. dataset avg rank ↓ example prompt example responses persona-chat 3.67 i know alotta women and i'm sure she remembers me. oh my goodness, i don't know her spolin 3.41 yeah, she's a bit of a mystery. persona-chat+spolin 3.17 so you remember her? i remember her in the shower. gold 1.92 she does. from when you were a boy. cornell 3.73 i made this beautiful salmon mousse that just looked like a puddle of spit. what? spolin 3.39 and it's delicious! cornell+spolin 3.34 that's the kind of thing you do when you're in love. gold 2.01 it was genius. it's making me hungry thinking about it. dailydialog 3.37 excuse me. is anybody in here? i'd like a bagel. hello? is anybody in here? i'm sorry, sir. i'm not in the mood. i'm not in the mood. spolin 3.32 i'm in here. i'm just trying to make sure i can get a bagel. dailydialog+spolin 3.31 oh, yeah, the guy who left the bagel. gold 1.87 i can help you. the problem is that the bagels are burned. @xcite crowdsourced 162k the ubuntu dialogue corpus @xcite ubuntu chat logs 7m twitter triple conversations @xcite social media 6k opensubtitles @xcite subtitles 441m sentences subtle (eng) @xcite subtitles 3.3m pairs london-lund corpus @xcite various sources 500k words london-lund corpus 2 @xcite various sources 500k words spolin (yes-and only) ## related work many works have identified the same issues of repetitive or non-committal responses generated by neural conversational systems that are at least partially related to the lack of sufficiently high quality yes-ands we deal with in this work; approaches that mitigate these problems vary. the majority of recent works focus on diversifying the responses by modifying the training and decoding objectives @xcite @xcite @xcite . other methods introduce latent variables to encourage diversity @xcite . some explore methods of re-weighing training instances that encourage diversity @xcite @xcite . our approach is complementary to all the model-based approaches described here, as it simply deals with the production of a particularly useful corpus, that can be used to fine-tune on top of these methods. we provide a survey of publicly available textbased datasets frequently used for open-domain dialogue systems and discuss their limitations for our purpose of generating grounded responses (see dailydialog is a collection of multi-turn dialogue with manually annotated emotion and intent labels @xcite . danescu-niculescu-mizil and lee (2011) created the cornell movie-dialogs corpus, a compilation of dialogue sequences paired with meta data about the movie and characters. persona-chat provides dialogue sequence coupled with corresponding personas @xcite . the ubuntu dialogue corpus contains 1 million dialogue turns extracted from ubuntu chat logs, which discuss ubuntu-related technical support @xcite . the twitter triple corpus is a dataset of 4k dialogue triples extracted from twitter @xcite . opensubtitles is a huge collection of subtitles that span various genres, but the absence of speaker turn annotations make it difficult to modify into dialogue format @xcite . @xcite use heuristics to reformat opensubtitles into dialogues with some limited success. @xcite illustrate grounding in conversations with examples from the london-lund corpus @xcite , a corpus of full conversations annotated with prosodic and paralinguistic features. a second version of the corpus was compiled with the same annotations standards as the first using more recent spoken and text data @xcite . these corpora were not collected with the criteria for yes-ands in mind. even for datasets with dialogue taking place in a similar domain as improv, they naturally contain only a small proportion of yes-ands. however, the relatively large sizes of these datasets still make them useful for dialogue systems. they can be used effectively for grounded conversations if the yes-ands or other desirable dialogue acts can be filtered out or given higher weights in training to enforce their characteristics in the responses generated. our data collection approach is similar to the method of @xcite , which formalizes the bootstrapping mechanism of iteratively improving a classifier and label unlabeled data. the main difference from the yarowsky algorithm and our approach is that, rather than using a fully automated process for increasing training data, we use a probability threshold to regulate recall, followed by human judgment to ensure high precision. apart from @xcite there have been other taxonomies of grounding. for example, traum (1999) considers six categories; among these are acknowledge and continue, which, taken together, map nicely to yes-and. @xcite and @xcite note the importance of establishing common ground in improv. ## conclusion inspired by yes-ands in improv, we carefully construct spolin, a collection of dialogue pairs with responses that are not only coherent with dialogue context but also initiate the next relevant contribution. we extract high-quality yes-ands from spontaneanation and build a classifier with them, which is then used to mine additional yes-ands from the cornell movie-dialogs corpus. we further use our mining technique to elicit a corpus of more than 68,000 yes-and turn pairs, easily the largest collection of this dialogue act known to exist. from human evaluations of dialogue models trained with various data configurations we find that spolin is useful-when including it we are able to build models that can generate yes-ands more consistently than when we leave it out. nevertheless, our models are still inferior at producing good yes-ands when compared to professional improvisers. we plan to continue our data-driven approach for grounded conversations by expanding our dataset through our iterative data collection process with other larger text-based open-domain dialogue corpora and extend our work to model and collect longer conversations exhibiting more complex improv-backed turns.