Venue: ACL 2020

# Grounding Conversations with Improvised Dialogues

Hyundong Cho and Jonathan May

Information Sciences Institute

University of Southern California

{jcho, jonmay}@isi.edu

###### 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 (improvo) 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.

## 1 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_[3]. Without coordination, partners may establish different or conflicting world states, leading to solipsism in the best case and conflict in the worst. Clark:98a, 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,1 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 improrov performances, there is a high proportion of these turns embedded in improv dialogue, which helps ensure scenes are coherent and interesting.

Figure 1: Explicit (top) and implicit (bottom) examples of _yes-ands_ in the **SPOLIN** corpus. The text highlighted in light blue reflects acceptance of the context established in the prompt (“yes”) and the text highlighted in orange initiates a new relevant contribution to the dialogue (“and”).



Open-domain neural dialogue systems, by contrast, specifically lack coherence and interestingness. They commonly repeat previous utterances [16] or generate non-committal, generic statements such as _I don't know_ that are logically coherent as a response but preempt further conversation [20; 34; 58]. 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 [7]. 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 [9; 10] or generate improvised stories [11], but development of improvocapable 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 [22] 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 [15].

Footnote 2: The art form has long roots, extending to the Italian _Commedia dell’arte_ tradition from the 16th century and fares from the Roman era, but we constrain our focus to the post-20th century form developed and championed by e.g. Keith Johnstone [10], Del Close [1], and our corpus’ namesake, Viola Spolin [23]. 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 backnymn it in recognition of her influence.

Footnote 3: One exception, the long-running TV show _Whose Line Is It Anyway_, has, despite a large number of episodes, surprisingly little continuous improvised dialogue, due to the rapid-fire nature of the program.

Therefore we set our objective as collecting _yes-and_-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 ImprovatioN_ (**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:

* We carefully curate _Selected Pairs Of Learnable ImprovisatioN_ (**SPOLIN**), the first large-scale corpus of _yes-and_ dialogue acts, sourced from improv and movie dialogues.
* We iteratively build a high-precision _yes-and_ classifier, which we use to mine additional _yes-and_s from dialogue corpora with high volume but low _yes-and_ density.
* We fine-tune existing open-domain conversational models with our corpus and confirm via human evaluations that this approach improves creative grounding.
* We release our models and data for public use, including a 64,000 turn pair extension of the core **SPOLIN**, at [https://justin-cho.com/spolin](https://justin-cho.com/spolin).

## 2 Data Collection

Our data collection has five stages:

1. Manually extract _yes-and_s from a rich corpus of improv to obtain an initial set of _yes-and_s.
2. Construct a _yes-and classifier_ from the corpus of collected _yes-and_ data and negative examples.
3. Use the classifier from step 2 to automatically extract _yes-and_ candidates from a much larger but sparser dialogue corpus 

4. If necessary, manually validate candidates before adding them to the _yes-and_ corpus.
5. Repeat from step 2 as needed.

An overview of this process is shown in Figure 2.

### Core _yes-and_ Collection from _Spontaneousation_

We select the _Spontaneanation4_ podcast as a source of concentrated _yes-ands_ for its relatively noise-free recording quality and high-quality volume of broad domain improv dialogue. Each episode of this podcast includes an approximately 30 minute-long improv session performed by professional improvisers. Over its 201 episodes, we identified a total of 43K lines of useful spoken dialogue. Footnote 4: [https://www.earwolf.com/show/spontaneanation-with-paul-f-tompkins/](https://www.earwolf.com/show/spontaneanation-with-paul-f-tompkins/)

Given the confluence of a lack of objective reality, and uninterrupted multiturn dialogue, the improvisers mostly abide by the _yes-and_ principle, and therefore _Spontaneanation_ is a rich resource for natural, high-quality _yes-and_s. As it exists only in audio form, and automatic transcription services are too noisy for high quality annotation use, we ask Amazon Mechanical Turk workers (Turkers) to listen to the improv sessions, view Amazon Transcribe preliminary transcriptions, and re-transcribe all of the _yes-and_s that they hear using our transcription interface, shown in Figure 3. The interface is based on oTranscribe, an open-source transcription service. Although the quality of transcriptions is poor, we find that including them assists the Turkers in identifying speaker turns and also understanding parts that are sometimes incomprehensible without helping context.

#### 2.1.1 Recruiting Quality Crowdworkers for Difficult Annotation Tasks

One of the main challenges for the data collection process is to recruit competent Turkers who are able to develop a good understanding of the _yes-and_ principle. We actively recruit potential annotators to our task by inviting denizens of the sub-Reddit TurkerNation, rather than simply inviting workers through Amazon's native task posting interface based on HIT approval rate and total number of HITs approved. Our approach enables more human-level engagement, making it easier to determine Turkers' English fluency and experience with improv. To ensure their competence,

Figure 3: Amazon Mechanical Turk interface for transcribing _yes-and_s from _Spontaneanation_ episodes. Approximate transcriptions with speaker turns and time stamps generated from Amazon Transcribe are provided for additional guidance.

Figure 2: An illustration of the _yes-and_ collection workflow. The core **SPOLIN** corpus comprises _Spontaneanation yes-ands_ and _Cornell yes-ands_ (in blue boxes). However, **SPOLIN** can be augmented by including other general-purpose dialogue corpora in place of _Cornell_ in this workflow, as described in Section 5.

 

Turkers first read _yes-and_ guidelines (in the appendix) then demonstrate their level of understanding through qualification Human Intelligence Tasks (HITs), which test whether the candidates can identify if a _yes-and_ exists in a 30 second audio segment and transcribe it if there is one. s

Even after inviting Turkers for the actual HIT of transcribing _yes-and_s, we frequently monitor the quality of the data they collect and provide feedback for incorrectly identified _yes-and_s. Apart from base pay for each episode they work on, we provide incentives for extracting more _yes-ands_. The pay for our HITs averages well above California minimum wage. From all of the episodes, we extract 10,959 _yes-and_s, indicating about 25% of the total number of dialogue turns in _Spontaneousation_ are _yes-and_s.

### Guided Extraction from the _Cornell Movie-Dialogs Corpus_

Although larger than any improv corpus, let alone _yes-and_ corpus known to date, we seek to increase our corpus volume from 10,959 turn pairs. The _Cornell Movie-Dialogs Corpus_[46] contains 304,713 turns, nearly an order of magnitude more than _Spontaneanation_, and it is one of the closest in domain to improv among existing dialogue datasets. However, a sample annotation of 300 randomly selected turn pairs by Turkers reveal only 11.1% of pairs are _yes-and_s. We thus use the already-collected _yes-and_s to probe _Cornell_ for likely candidates, to speed the search process. Recent developments of language models pre-trained on massive text data enable the training of high-accuracy models for down-stream tasks even with a small number of samples, by leveraging the contextualized embeddings that these models learn [8, 10]. We thus fine-tune an initial BERT-based sequence classifier based on the implementation of Wolf et al. wolf:etal:etal:2019 with the _yes-and_s from the _Spontaneousation_ episodes to determine if a given dialogue pair is a _yes-and_, using a high threshold (initially, a 95% probability of being _yes-and_) to bias for precision. We ask Turkers to validate the turn pairs identified by the classifier and add the validated pairs to our _yes-and_ corpus. This procedure can be iterated.

For the first iteration, we train the classifier with a balanced number of non-_yes-and_s chosen by random sampling from _Cornell_, a reasonable assumption due to the relatively low concentration of _yess-and_s observed. The same Turkers that extracted _yes-and_s from _Spontaneousation_ are invited to validate the _yes-and_ candidates filtered out by the classifier using the interface shown in Figure 4. In order to ensure consistent annotation standards among Turkers, they are given a small number of overlapping HITs against which we validated. For 90 samples of unfiltered _yes-and_ candidates from _Cornell_, the two workers yield a reasonably high Cohen's \(\kappa\) value of 0.74. Turkers are paid at rates consistent with their rates on the extraction-from-_Spontaneousation_ task.

After the set of _Cornell yes-and_ candidates are validated, the _yes-ands_ and non-_yes-and_s are added to the training set to train a new classifier, and the same process is repeated. We hold out 500 dialogue pairs from each subcategory (i.e. _Spontaneousation__yes-and_s) as the development set for monitoring the classifier's performance after each iteration. We incrementally lower the classification threshold for choosing a _yes-and_ candidate as the classifier improved. We set this threshold on each iteration except for the first by retrospective evaluation of the classifier on the actual _yes-and_ candidates' labels from previous iterations. The threshold with the highest F1 score is chosen to filter new _yes-and_ candidates to be validated.

We balance each progressively larger corpus with negative sample turn pairs, which are either randomly selected from _Cornell_ (round 1), selected

\begin{table}
\begin{tabular}{l r r r r} Iteration & 1 & 2 & 3 & 4 \\ \hline _Spontaneousation_ + & 10,459 & 10,459 & 10,459 & 10,459 \\ _Spontaneousation_ – & - & - & 3,225 & 5,587 \\ _Cornell_ + & - & 3,327 & 8,464 & 12,220 \\ _Cornell_ - & 10,459 & 13,786 & 15,698 & 17,092 \\ Total Training Samples & 20,198 & 27,572 & 37,846 & 45,358 \\ Dev Set Acc. (Spont) & 80.9\% & 73.6\% & 71.6\% & 73.0\% \\ Dev Set Acc. (_Cornell_) & 52.2\% & 56.8\% & 62.1\% & 64.5\% \\ \hline Confidence Threshold & 95\% & 70\% & 50\% & 50\% \\ New Extraction Volume & 12,360 & 12,802 & 5,150 & 3,515 \\ New Proportion of _yes-ands_ & 26.9\% & 44.0\% & 72.9\% & 78.4\% \\ \end{tabular}
\end{table}
Table 1: Iterative data collection results over _Cornell_. + indicates _yes-and_s and – indicates non-_yes-and_s. These counts exclude 500 turns collected from each of _Spontaneousation_ and _Cornell_ to form the validation set. The New Extraction Volume row indicates the new number of _yes-and_ candidates identified with the given confidence threshold, and the New Proportion of _yes-and_ row show as a percentage how many of these candidates were indeed evaluated as _yes-ands_ by Turkers. The proportion of _yes-and_s increases after each iteration despite the lower confidence threshold used to filter the new predictions with the updated classifier.

 

from the rejected-but-extracted turn pairs from _Cornell_ (round 2 and later), or sampled from non-_yes-and_ turn pairs in _Spontaneanation_ formed by random coupling of prompts and responses of the _Spontaneousanion yes-ands_ (round 3 and later). The latter forces the classifier to make decisions based on semantic features relevant to a _yes-and_ instead of only stylometric features in _Spontaneanation__yes-and_s. We stop this iterative process after four rounds, when fewer than 5,000 new _yes-and_ candidates are identified by the classifier, yielding a total corpus size of 26,435 _yes-and_s and 23,938 negative samples. An overview of this iterative process is summarized in Table 1. The negative sampling procedure, while somewhat ad-hoc, ultimately provides a mix of turn pairs from both corpora that is sufficient to allow extraction of _yes-and_s from new corpora at high precision rates, and is sufficient for our goals.

### Additional Notes on _yes-and_ Criteria

Although the concept of a _yes-and_ is easy to define and understand, there are borderline cases between a _yes-and_ and a non-_yes-and_ that make the validation phase more difficult than originally expected. One of the cases that confused Turkers in the earlier stages of data collection is the case of _yes-but_s. A _yes-but_ is a _yes-and_ with a response that is coherent with the provided reality, but does not appear to provide an affirmative acceptance of a suggestion given in the prompt. These are different from _contradictions_ that do not align with the previously established reality. In addition, there are instances where the response is a _yes-and_, but is accepted by a speaker other than the one to whom the prompt is directed. Some _yes-and_ responses initiates a repair of a problem encountered while accepting the prompt, due to a confusion or a possible inconsistency, by asking for clarification Clark and Schaefer [1989]. While these responses may not strictly _establish_ more detail, they provide information for ultimately establishing new information. We elide these edge cases under the umbrella category _yes-and_ in **SPOLIN** as they further our top-level goal of providing relevant, actionable turn responses. Examples of some of these subtle differences are shown in Table 2.

## 3 Dataset Analysis

In order to provide a better understanding on the characteristics of our corpus, we annotate 200 _yes-and_s and 200 non-_yes-and_s in **SPOLIN**'s development set to categorize them into specific _yes-and_ or non-_yes-and_ types.

We classify _yes-and_s into explicit _yes-and_s, implicit _yes-and_s, or _yes-but_s. Only 15% of all _yes-and_s are explicit _yes-and_s, containing phrases such as "Yeah" or "Sure" that reflects agreement. Even with such phrases, identifying explicit _yes-and_s 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-and_s that contain phrases affirming agreement but have no contributions or have new contributions that lack relevance. The majority (78%) of _yes-and_s are implicit _yes-and_s, meaning that the agreement is implied, often in a subtle manner. The remaining 7% are _yes-but_s.

Non-_yes-and_s 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

Figure 4: Amazon Mechanical Turk interface for validating _yes-and_ candidates determined by the _yes-and_ classifier. Turkers are asked to correct minor errors in grammar, spelling, and punctuation for qualifying _yes-and_ candidates, which are then categorized as ‘Typo/Fix.’



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-and_s and non-_yes-and_s are shown in Table 2.

The main focus of our work is on _yes-and_s, 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 Table 3.

## 4 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 Wolf et al. (2019b)), which achieved state-of-the-art performance on _Personachat_ for the ConvAI-2 dialogue competition Zhang et al. (2018). We fine-tune the models using two learning objectives, which we weigh equally in calculating loss:

1. Predicting the next word.
2. Predicting the next correct candidate that best fits the dialogue given the dialogue history.

The language modeling component uses pre-trained 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 Holtzman et al. (2020) 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.

### Data Configurations

For our experiments, we use several established dialogue datasets as baselines, namely _Persona-chat_Zhang et al. (2018), _Cornell_Danescu-Niculescu-Mizil and Lee (2011) (the unfiltered corpus out of which we extract _yes-and_s, as described in Section 2.2), and _DailyPiolog_Li et al. (2017b). Each of these is an English-language open-domain casual conversation corpus with 100k-300k turns. For each of these datasets, we either simply finetune on that dataset, or fine-tune and then further

\begin{table}
\begin{tabular}{l l c r} Type & & Example & \\ \hline  & Explicit & P: Does this map look homemade to you? & 15\% \\  & & R: Yeah, it looks like someone without a grasp of English drew it. & \\ \multirow{6}{*}{_yes-and_} & Implicit & P: Alright, pull up that plate so I can take a picture. & 78\% \\  & & R: Sorry, the coleslaw is definitely giving off a lot of glare. & \\  & _yes-but_ & P: We all must say the chant that we say to the king. & \\  & _yes-but_ & R: No, it’s too erotic, please don’t. & 7\% \\  & Contra & P: Hey, hey, aren’t you afraid you’ll burn out a tonsil? & \\  & & R: Tonsil? Me? No! Me burn a tonsil? My tonsils won’t burn - As life’s corners L.. & 5\% \\ non-_yes-and_ & & P: I feel different right now. & \\  & Other & R: You wait and see. You’re going to marry a big hero! & 95\% \\ \end{tabular}
\end{table}
Table 2: Examples and proportions of _yes-and_ and non-_yes-and_ types from annotations of 200 _yes-and_s and non-_yes-and_s in **SPOLIN’s** development set. Determining whether a given dialogue pair is a _yes-and_ or not is a non-trivial task, as the agreement or contradiction of the previous dialogue turn’s context is usually implicit.

\begin{table}
\begin{tabular}{l c c}  & _yes-and_s & non-_yes-and_s \\ \hline _Spontaneousation_ & 10,959 & 6,087\({}^{*}\) \\ _Cornell_ & 15,476 & 18,351 \\ \hline
**Total** & **26,435** & **24,438** \\ \hline \end{tabular}
\end{table}
Table 3: Composition of **SPOLIN**, including the development set. _yes-and_s and non-_yes-and_s from _Cornell_ are validated by Turkers. _“Spontaneantionn-yes-and_s are sampled from random combination of prompts and responses in _Spontaneantion yes-and_s to balance the dataset for training the classifier in the final iteration, as shown in the last column of Table 1.

 

fine-tune with **SPOLIN**. In another configuration, we also fine-tune directly with **SPOLIN** on top of GPT-2. The original GPT-2 implementation prepends the personalities given in _Persona-chat_ to the dialogue sequence input before tokenization. For fine-tuning to datasets apart from _Persona-chat_, we simply do not prepend any auxiliary information to the dialogue sequence input.

### Human Evaluation

Automatic metrics that rely on n-gram overlap, such as BLEU, ROUGE, and METEOR, are often used for generative models when there is little variability in the target output Papineni et al. (2002); Lin (2004); Banerjee and Lavie (2005). However, there can be a wide variety of responses that qualify as a good _yes-and_, a problem common to open-domain generation tasks. An adequate evaluation of our models requires assessing the main _yes-and_ criteria: agreement with the context and the quality of the new relevant contribution, both of which are not feasible with the aforementioned metrics. Therefore, we ask human evaluators to compare the quality of the _yes-and_s generated by various models and the actual response to the prompt in **SPOLIN** that is used as the input.

We ask human evaluators to rank a set of four responses given a prompt, comparing the responses of a model trained only with **SPOLIN**, a model trained with an existing dialogue corpus, a model trained with both, and the actual response pair from the development set, denoted as "Gold." These four responses are randomly ordered for each question to prevent evaluators from developing a bias for responses that frequently have a good or poor response in a set order, as shown in Figure 5. The evaluators are permitted to provide the same rank for different responses if they are equal in quality. This evaluation set contains 100 such prompts, and each is evaluated twice by different evaluators. The results of the average ranking and some of the examples generated by the models are shown in Table 4.

Results show that models trained only with **SPOLIN** or with **SPOLIN** and another dialogue dataset are preferred to the models trained only with another dialogue dataset, although in the case of _DailyDialog_, the average ranking improves only by at most 0.06 after fine-tuning with **SPOLIN**. However, even the responses generated by models trained with **SPOLIN** are not ranked as well as the actual responses in the development set, indicating our models are still inferior to professional human improviser quality.

## 5 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 Lison and Tiedemann (2016) collection. As OpenSubtitles contains undesirable material, such as subtitles for media with minimal dialogue, we instead mine from the (3.3M sentence) _SubTle_ corpus Ameixa et al. (2013), 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-and_s 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 _Spontaneousation_ and _Cornell_, so once the proportion of utterances longer than the average length of in _Spontaneousation_ and _Cornell_ (18.5 words) is less than 40%, we stop further collection in the remainder of the dataset. **SPOLIN**-extended is available in the same public repository as **SPOLIN**. Details of the iterative process as applied to _SubTle_ are in the appendix.

Figure 5: Interface used by human evaluators to rank responses based on their quality as a _yes-and_, where a rank of 1 is most preferred. The correct ranking is shown for this example. The option ranked 1 is a _yes-but_: it does not reject a reality but rather rejects a suggestion and provides refining information that is most coherent to the prompt.

 



## 6 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-and_s 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 Li et al. (2016, 2017, 2016); Xu et al. (2017); Shao et al. (2017). Other methods introduce latent variables to encourage diversity Serban et al. (2017); Zhao et al. (2017). Some explore methods of re-weighing training instances that encourage diversity Liu et al. (2018); Lison and Bibauw (2017); Du and Black (2019).

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 text-based datasets frequently used for open-domain dialogue systems and discuss their limitations for our purpose of generating grounded responses (see Table 5 for an overview).

_DailyDialog_ is a collection of multi-turn dialogue with manually annotated emotion and intent labels Li et al. (2017). Danescu-Niculescu-Mizil and Lee (2011) created _the Cornell Movie-Diallogs Corpus_, a compilation of dialogue sequences paired with meta data about the movie and characters. _Persona-chat_ provides dialogue sequence coupled with corresponding personas Zhang et al. (2018).

\begin{table}
\begin{tabular}{l l|p{34.1pt} p{142.3pt}} Dataset & Avg Rank \(\downarrow\) & Example Prompt & Example Responses \\ \hline _Persona-chat_ & 3.67 & I know alotta & oh my goodness, i don’t know her \\ \hline
**SPOLIN** & 3.41 & Women and I’m sureshe remembers me. & So you remember her? Iremember her in the shower. She does. From when you were ab boy. \\ \hline Gold & 1.92 & & What? And it’s delicious! \\ \hline _Cornell_ & 3.73 & I madethis beautiful salmon mousse that just looked like a puddle of spit. & That’s the kind of thing you do when you’re in love. It was genius. It’s making me hungry thinking about it. \\ \end{tabular} 
\begin{tabular}{l l|p{34.1pt}}  & & I'm sorry, sir. I’m not in the mood. \\ \cline{2-3}  & & Exuse me. Is anybody in here? I’m in here. I’m just trying to make sure I can get a bagel. \\ \cline{2-3}  & & Hello? Is anybody in here? I can help you. The problem is that the bagels are burned. \\ \end{tabular}
\end{table}
Table 4: Average human ranking of responses to prompts from _Spontaneousation_ generated by models trained with **SPOLIN**, an existing dialog corpus, or both, based on the _yes-and_ criteria. Rank is scaled from 1 to 4, lower is better.

\begin{table}
\begin{tabular}{l l l c} Dataset & Source & Size\({}^{*}\) \\ \hline DailyDialog Li et al. (2017) & Crowdsourced & 104K \\ Cornell Movie-Dialogs Corpus Danescu-Niculescu-Mizil and Lee (2011) & Movie scripts & 304K \\ Persona-chat Zhang et al. (2018) & Crowdsourced & 162K \\ The Ubuntu Dialogue Corpus Lowe et al. (2015) & Ubuntu chat logs & 7M \\ Twitter Triple Conversations Sordoni et al. (2015) & Social media & 6K \\ OpenSubtitles Lision and Tiedemann (2016) & Subtitles & 441M sentences \\ SubTle Eng) Ameixa et al. (2013) & Subtitles & 3.3M pairs \\ London-Lund Corpus Greenbaum and Savrutvik (1990) & Various sources & 500K words \\ London-Lund Corpus 2 Poldvere et al. (2017) & Various sources & 500K words \\ \hline
**SPOLIN** (_yes-and_ only) & Improv, Movie scripts & 26K pairs \\
**SPOLIN-extended** (_yes-and_ only) & Improv, Movie scripts, subtitles & 68K pairs \\ \end{tabular}
\end{table}
Table 5: A survey of publicly available English language text-based corpora frequently used for open-domain dialogue systems. The last two rows are our contribution. \({}^{*}\)Size is measured as the number of total utterances (dialogue turns) unless otherwise specified.



The _Ubuntu Dialogue Corpus_ contains 1 million dialogue turns extracted from Ubuntu chat logs, which discuss Ubuntu-related technical support Lowe et al. (2015). The _Twitter Triple Corpus_ is a dataset of 4K dialogue triples extracted from Twitter Sordoni et al. (2015). _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 Lison and Tiedemann (2016). Ameixa et al. (2013) use heuristics to reformat OpenSubtitles into dialogues with some limited success. Clark and Schaefer (1989) illustrate grounding in conversations with examples from the London-Lund Corpus Greenbaum and Svartvik (1990), 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 Poldvere et al. (2017).

These corpora were not collected with the criteria for _yes-and_s in mind. Even for datasets with dialogue taking place in a similar domain as improv, they naturally contain only a small proportion of _yes-and_s. 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-and_s 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 Yarowsky (1995), 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 Clark and Schaefer (1989) 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_. Magerko et al. (2009) and Fuller and Magerko (2010) note the importance of establishing common ground in improv.

## 7 Conclusion

Inspired by _yes-and_s 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-and_s from _Spontaneousation_ and build a classifier with them, which is then used to mine additional _yes-and_s 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-and_s 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.

## Acknowledgments

Many thanks to Nanyun Peng and Xinyu Wang for key contributions in a preliminary study, to Paul F. Tompkins, Colin Anderson, and Earwolf for allowing us to include _yes-and_s extracted from _Spontaneanation_ in **SPOLIN**, to Paul Elsberg, Risa Harms, P.T. McNiff, and Peter Schell for initial inspiration, and to Jordan Boyd-Graber for feedback on the final draft. This material is based on research sponsored by the AFRL and DARPA under agreement number FA8650-18-C-7878. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the AFRL, DARPA, or the U.S. Government.

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## Appendix A Appendix

### _yes-and_ Guidelines for Turkers

We provide detailed annotation guidelines, shown in Figures 6-9, to the Turkers as a result of having continuous discussions with them and monitoring their submissions. Contrary to our expectations, it is difficult to make a binary decision on whether a dialogue turn is a _yes-and_ or non-_yes-and_, and therefore these fine-grained details are crucial for collecting _yes-and_s in **SPOLIN**.

### Iterative data collection results for _SubTle_

Due to _SubTle_'s relatively large size, we split _SubTle_ into 20 equal blocks that each contains 10,486 dialogue turns. Note that every successive iteration of _SubTle_ was not performed on the same block but on the next block. This is different from _Cornell_, for which every iteration is on the same set of dialogue turns. This difference is not due to any characteristics in the dataset but because of practical reasons arising from the size of the _SubTle_ corpus.

The first extraction proportion for _SubTle_ is low because of the prevalence of self-_yes-ands_ in this corpus. Self-_yes-and_s are prompt and response pairs that evidently originate from the same speaker but otherwise meet the criteria of a _yes-and_. There are many incorrectly combined dialogue turns that actually come from the same speaker because of the heuristics employed for building _SubTle_. By providing labeled self-_yes-and_ as negative samples, the classifier quickly learns to remove these self-_yes-ands_, leading to a significantly higher proportion of _yes-and_s in subsequent iterations. This is demonstrated in the specifics of the additional iterations, which are shown in Table 6.

 



### Objective

The objective of this task is to collect a corpus consisting of "Yes, and..." type dialogue pairs (henceforth simply "Yes, and?), which will be used to train neural network models for creative language generation. For each of these HTs, you are expected to identify all the "Yes, and" you hear from an inprov session within a podcast episode.

#### Definition

"Yes, and.." is a rule-of-burnb in improviational comedy that suggests that a participant in a dialogue should accept what mother participant has stated ("Yes") and then expand on that line of thought or content ('and..."). 1 in short, a "Yes, and" is a dialogue exchange in which a speaker responds by adding new information on top of the information/setting that was constructed by another speaker.

Footnote 1: [https://www.sds.org/](https://www.sds.org/)

Note that a "Yes, and" does not require someone explicitly saying "yes, and..." as part of a dialogue exchange, although it could be the case if it agrees with the description above. There are many ways in which a response could implicitly agree to the prompt without specifically saying yes, and.."

#### Basic Structure of a "Yes, and"

#### Prompt

**"Yes, and" Response**

Provides some information.

(Either as a statement or a question)

new relevant information.

If this is your first time doing this HT or if you are unsure as to what qualifies as a "Yes, and", read through the "Yes, and" Annotation Examples section to develop a better understanding for this task.

#### Example

Given the following dialogue sequence, those selectively shown in the table below are examples of "Yes, and":

1. [A] Well, which one speaks to you?
2. [B] Well, who can over here be the northwest corner. The crow with the beauty eyes that seem to follow you. Something about it. It reminds me of something.
3. [C] Well, though what I reynolds me of it reminds me of that look but comes over Dears face when he’s done something terrible, when he sayn..
4. [H] When he says, "No, Memory, invent team may room."
5. [A] That's right!
6. [B] Is a row body, but still the face of our child. Weit give you fully dollars for it.
7. [C] That's exactly what I am asking. There's a sign that says fifty.
8. [B] didn't see the sign. To what just a lacky gives. From a little bit upscribie.
9. [A] Why let being modest. But very specific.
10. [B] In its true. In fact, all the women in my family, we have abilities.
11. [C] Please tell me a physicist vision you have had that has come true.
12. [A] Oh, tell about, remember that time we were out with the kids and we were going to go to Six Flugs, and then you said we should't get on the ruler coaster.

#### Vres, and" Dialogue pair

**[B] Well, the one ever here in the northwest corner. The crow with the beauty eyes that seem to follow you. Something about it. It reminds me of something.**

[A] Yeahil know what I reminds me of. It reminds me of that look that comes over Dears' face when he's done something terrible, when he says...

[A] Yeahil know what I reminds me of. It reminds me of that look that comes over Dears' face when he's done something terrible, when he says...

[B] When she says, No, Memory, invent clean try room."

Speaker B establishes that she is interested in a painting with a crown in It and what it reminds her of something. Speaker A adds on to her remark and suggests details of what it reminds him of.

Speaker B establishes that the pairing remarks hint of Dean's face when he's done something terrible, and Speaker A provides a detailed situation in which Dean makes the face thrt Speaker B was mentioned.

Speaker B suggests that she will pay $50 for the painting. Speaker C agrees with her price and adds on the fact that there is a sign with the price on It. Agregents with the price is not necessary but rejecting a setting Speaker B has established whether he refourge, or example, "I want $50 for It. Twenty a sign with the price." is okay but "I know you don't have $50" does not qualify as a "Yes, and".

Speaker B understands that there is a sign that she ddirut see, and suggests that she has psychotic athletes that allowed her to guess the price correctly.

Speaker C suggests that Speaker B has psychic abilities. Speaker A, knowing of Speaker B's psycho powers, interrupts and suggests a specific instance in which she demonstrated perpowers.

Figure 6: Explanation of the objective and _yes-and_ in the _yes-and_ guideline provided to Turkers.





**Label space**

Here is a list of subcategories for a "Yes, and" and for those that are not. You **do net** need to specify subcategories for this task and the following information should only be used as an extra guideline to help you better identify "Yes," and's. These labels are also used in the "Yes, and" Arroated Example section for succinct explanations.

* [leftmargin=0pt]
* **Yes, and subcategory**
* **Yes-state:** specifics provided by the prompt are refined
* **Yes-but:** acknowledges and adds, but rejects a specific suggestion offered by the prompt, often with 'but' or 'however'; it's different from contradiction in that the reality itself isn't rejected but an offer within it isn't taken.

### Non-"Yes, and subcategory

* [leftmargin=0pt]
* No (no-info): Not contradictory, but does not add more information to that added by the prompt
* No (ignore): The response ignores statements in the prompt; there is no plausible way the response and prompt could be connected
* No (contrat): The response may add some information but contradicts the reality established by the prompt

## References

* [1] T. A. Armstrong, M. C. B. Anderson, J. R. L. Hogan, and D. S. K. Smith. _The Impact of the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact on the Impact of the Impact of the Impact on the Impact of the Impact of the Impact on the impact of the Impact of the Impact on the



## Common Mistakes

Here are some common mistakes that we have seen in previous HIT submissions. Make sure to understand why

**Common mistake 1: Write prompts as statements or questions that do not introduce information**

Undesirable answer:

P: What do you think?

R: It looks nice, but it's like the eyes are following me.

Desirable answer:

P: Why do you want to do that? If they are alive and they have been caged their entire lives, why do you want to have them suffer?

R: Caged? Theyve been spoon-fed all kinds of luxuries in that castle. It's time for them to learn the tough lessons of life.

Although the prompt is a question, it establishes that "they' were alive and caged their entire lives and that the following speaker wants them to suffer.

**Common mistake 2: Response that simply agrees to the prompt but does not significantly add/refine information provided by the prompt**

Undesirable answer:

P: They left me a note to read to you.

R: Oh did they? Let me see what that says.

Desirable answer:

P: They left me a note to read to you.

R: Oh did they? Let me see what that says. Is it that red piece of paper in your hands?

The added question in the response establishes additional information that the previous speaker is holding onto a red piece of paper in her hands. (may or may not be the note)

**Common mistake 3: Response logically follows the prompt but does not add/refine information provided by the prompt**

Undesirable answer:

P: God, I'm gonna punch you so hard in the stomach!

R: YouVe got to warn me first.

Desirable answer:

P: God, I'm gonna punch you so hard in the stomach!

R: YouVe got to warn me first. I have a bad tummy right now and I don't want there to be an accident in your carpeted living room.

The original answer could also be argued as a "Yes, and", but it is weak in saying that a warning is necessary is more an approval of the punch he's about to take than an addition of information. A better answer would provide a reason why a warning is necessary.

Figure 8: Common mistakes that Turkers made in the early stages of data collection were corrected and added to the guidelines to aid new Turkers.





**X: I hate to quibble with you Rob, but she married her first boyfriend. Kevin Bannister. Y: You gotta be kidding me. Annotation: No (no-info). No contradiction, simply a mild expression of incredility. A good answer would have included something like "Kevin Bannister the circus clown?" or "can't believe Jill would do that". X: Well, I wish you'd stop criticizing and picking on her. Y: Forgive my crudity, darin! All l'm saying is that a girl who would wear clothes like that is going to get in trouble sooner or later.** Annotation: Yes (strong). The "forgive my crudity" acknowledges that the respondent is indeed 'criticizing and picking on her' and the "all l'm saying" part explains the behavior further. X: With all she was doing!. With all the shit she kept doing! You stayed stuck to that bitch's ass and you wouldn't let go. Y: I know about how she was like. But we was different. It's the only person she talked to about it. How she's abused. Terrible things, Gill, just terrible... Annotation: Yes (strong) [not particularly funny, but that's not relevant]. Explains why Y "stayed stuck". Adds more to the character. X: Yeah, man. Let's throw a bachelor party with drugs, booze and broads. Y: Yeah. Right. All the things that make life worth living. Annotation: No(no-info). Simply acknowledges X. A good answer could have included "Oh, I know where we can get some ketamine!" or "Let's have it in vegas!" X: Instead of busting my chops you should do something about that girl. Fire her. Or something. Y: Lisa's an extremely valuable member of the Skywire team. We've got our eyes on her. You keep yours on Milo. Annotation: No (contra) Definitely adds info but is combative and contradictory; rejects the assertions offered by X. A better answer (a yes-but) would have preceded what is provided with, e.g. 7 agree that Lisa has messed up, but". A true "yes, and" would have been "She's a valuable member of the team so we've been resisting, but yourre right, she's gone too far. Milo is too, so you should also fire him" X: Aw, come on Jerry. We've gotten all we're gonna get out of this place and its starting to rain. Y: Shit, it is only sprinkling and it's worth the trouble. Hold on for two seconds. Annotation: No (contra). Denies that it's not useful to stay around Note that this does acknowledge the rain ("it is only sprinking" supports "its starting to rain?) but per the guidelines, there must be no contradictions for a 'yes'. Additionally, this doesn't add anything concrete. A good answer would be e.g. "yeah, well it is only sprinkling but it's true, staying here is too much trouble". X: ...don't do it to me... Y: She came to talk to me... Annotation: No (ignore). Y is not responsive to X. A good answer might be "tm just going to give you a little novocaine so you won't feel it when we remove your tooth"

Figure 9: Annotated examples provided to Turkers for understanding the label space of the _yes-and_ transcription task.