SlowGuess/NAACL_2021_OCR huggingface dataset:
Table 3: Automatic evaluation results on the test set of ACCENTOR-SGD.
U:	I like to find some movies directed by Jonathan Levine.
A:	What type of movies you interested?
U:	I like comic movies which is great.
A:	① That's cool. There are 1 movies of your interest. What is your opinion about Long Shot?
U:	Long Shot is a good one.
A:	② I agree. Do you like to run the movie?
U:	I need to run the movie with subtitles.
A:	Please give confirmation details: Running Long Shot with subtitles. ③ Thank you for your help.
U:	Yes. Okay fine.
A:	The movie started playing. ④ It's a great comedy.
U:	I want to make a reservation at a restaurant around there.
A:	Do you have any choice of restaurant and city and what is a book- ing time you need?
U:	I need it in San Francisco at 17:30 and the restaurant is Aliment.
A:	Please give confirmation details: Table booking at Aliment in San Francisco at 5:30 pm on march 1st and booking is for 2 people. ⑤ Thank you.
U:	Fine. This is okay for me.
A:	Your booking is successful and location address is 786 Bush street. ⑥ I hope you will enjoy the rest of your evening.
U:	Thank you so much for your help.
A:	⑦ You are very welcome. Do you need any other help?
U:	No, thanks a lot.
A:	Have a great day.
Table 4: An augmented dialogue (with injection frequency in (0.4, 1]) that is less preferred than the unaugmented in terms of human evaluation (U: user; A: assistant; chit-chat is marked by circled numbers).
too talkative. (ii) There are inevitable annotation errors, and scenarios where whether a candidate is good or bad is subjective. A higher injection frequency means a higher chance of being affected by these factors. (iii) Since candidates are labeled independently, inter-candidate incompatibility may arise (e.g., expressing contradicted preferences), especially when we have a high injection frequency. Table 4 shows a real example to support our hypotheses. Specifically, ③ is labeled as good but is indeed not a suitable (or at least a questionable) candidate, supporting the hypothesis (ii). While ② and ④ are good candidates when we evaluate them separately, they may be less preferred if we assess them jointly because they convey the same meaning: "Long Shot is a good comedy." Having them together may appear incompatible (i.e., repetition) or sound verbose to the user, supporting the hypothesis (i) and (iii).
ACCENTOR-MultiWOZ. To investigate the flexibility of our data construction approach, we aug-
(a) Engagingness.
(c) Knowledge.
Figure 6: Comparisons between MultiWOZ 2.1 and ACCENTOR-MultiWOZ at the dataset level using ACUTE-Eval (\*\*: p-value  $< 0.005$ ).
(b) Interestingness.
(d) Humanness.
Figure 5: Human evaluation results on the test set of ACCENTOR-SGD using ACUTE-Eval (*: p-value < 0.05, **: p-value < 0.005).
MultiWOZ 2.1 vs. ACCENTOR-MultiWOZ (Win %)	(Win %)
Engagingness	10**	90**
Interestingness	11**	89**
Knowledge	13**	87**
Humanness	10**	90**
ment about 1K randomly sampled dialogues from another task-oriented dataset, MultiWOZ 2.1 (Eric et al., 2020) following the same steps as described in Section 2. Crowd workers label  $30.0\%$  of the candidates as good, which is lower compared with ACCENTOR-SGD  $(41.4\%)$  in Table 2). We attribute the difference to (i) the performance downgrade of the filter model since we do not re-train the model for MultiWOZ 2.1, and (ii) a higher chance of a chit-chat augmented response being too verbose to be good since the average number of tokens per system turn in MultiWOZ 2.1 is larger than that of SGD (17.3 vs. 13.1). Nevertheless, the augmented version (denoted as ACCENTOR-MultiWOZ) is significantly more preferred than the original, as shown in Figure 6, where we randomly sample 100 dialogues from ACCENTOR-MultiWOZ, augment all of their system responses that have chit-chat candidates labeled as good, and compare these augmented dialogues with the corresponding original dialogues.
4.2 Model Evaluations
Automatic Evaluations. We consider joint goal accuracy (Joint GA) and average goal accuracy (Avg GA) for evaluating belief states, act-slot F1
for evaluating action decisions, and two BLEU-4 scores (BLEU-4orig, BLEU-4aug) for evaluating system responses, where we use original (resp. augmented) system responses as references for BLEU-4orig (resp. BLEU-4aug). Table 3 summarizes the evaluation results. Since the test set of SGD contains unseen services (i.e., services not seen during training) designed to evaluate the model's generalizability, we report the results on all services (All) and seen services only (Seen) following Rastogi et al. (2020). Our proposed models generally achieve a similar task performance level compared with the SimpleTOD baseline. Unsurprisingly, the proposed models achieve lower BLEU-4orig and higher BLEU-4aug.
Human Evaluations. We turn to human evaluations for a more comprehensive measure of the response generation performance. We employ the same ACUTE-Eval metrics as we do in data evaluations. We randomly sample 100 dialogues from the test set of ACCENTOR-SGD. For each sampled dialogue  $D = \{u_{1}, s_{1}, u_{2}, s_{2}, \ldots, u_{n}, s_{n}\}$ , we pass  $u_{1}, s_{1}, \ldots, u_{i}$  to each model  $\mathcal{M} \in \{\text{SimpleTOD}, \text{SimpleTOD+}, \text{Arranger}, \text{Rewriter}\}$  to obtain its system response  $s_{i}^{\mathcal{M}}$  for the  $i$ -th system turn  $(1 \leq i \leq n)$ . Let  $D^{\mathcal{M}}$  represent  $\{u_{1}, s_{1}^{\mathcal{M}}, \ldots, u_{n}, s_{n}^{\mathcal{M}}\}$ . We ask evaluators to compare each pair of  $D^{\mathcal{M}_{1}}$  and  $D^{\mathcal{M}_{2}}$ , where  $\mathcal{M}_{1}, \mathcal{M}_{2} \in \{\text{SimpleTOD}, \text{SimpleTOD+}, \text{Arranger}, \text{Rewriter}\}$  and  $\mathcal{M}_{1} \neq \mathcal{M}_{2}$ . As shown in Figure 5, all of the chit-chat augmented models outperform the SimpleTOD baseline over four ACUTE-Eval metrics. Among the chit-chat augmented models, no one shows a clear win over the other two on the quantitative level. We show a full dialogue example comparing responses gener
SimpleTOD vs. Modified Arranger (Win %)
Engagingness	14**↓10	86**↑10
Interestingness	25**↓2	75**↑2
Knowledge	20**↓3	80**↑3
Humanness	20**↓9	80**↑9
Figure 7: Human evaluation results of the modified Arranger with controlled injection frequency  $(^{**}$  p-value  $< 0.005$ $\uparrow/\downarrow$ : increased/decreased win  $\%$  compared with the original Arranger).
ated by different models along with supplementary discussions in Appendix A.5.
Considering that the injection frequency affects human evaluations (Section 4.1) and that all our models do not explicitly control the injection frequency, we experiment with controlling the injection frequency by modifying Arranger to consider including chit-chat into the current turn only when the injection frequency from the first turn to the current turn is less than 0.3. Compared with the original Arranger, the modified Arranger achieves a higher win percentage over SimpleTOD, as shown in Figure 7. We leave further exploration of injection frequency for future work.
4.3 Limitations and Further Discussions
Approach. Our proposed strategy to augment task-oriented dialogue system responses with chitchat is simple, compared with how it emerges in human conversations, where both functionality and engagingness structurally intertwine with each other in a more complex fashion. Our proposed Rewriter model does have a modeling capability to compose both functions organically but is limited due to the dataset's target arrangement (i.e., concatenation of two separate components). Despite the limitation, our chosen design of "code-separation" has practical merits: we can easily extend the proposed approach to an existing production-level virtual assistant system as a modularized solution, and it has minimal interference to the user-perceived task success rate, a core metric widely adapted in virtual assistant systems. Another limitation of our work is that we only augment responses on the system side in our dataset, and the augmentations are independent of each other, whereas in real-life situations, users are also likely to make chit-chat, and the chit-chat between the user and the system should ideally be related to each other. We leave for future research addressing these limitations.
Evaluation. We follow the previous literature on evaluation and regard the four ACUTE-Eval metrics as the primary measure of the response generation performance in this work. However, there is a large overlap between the desired quality measured by different human judgment categories used in ACUTE-Eval. The four ACUTE-Eval metrics favor the same dialogue  $84.4\%$  of the time in our evaluation, indicating high correlations between these metrics. We leave the study of addressing this issue for future work.
5.1 Dialogue Datasets
Dialogue system research has been consistently supported by the development of new datasets. The Dialog State Tracking Challenge (DSTC) series (Williams et al., 2013; Henderson et al., 2014a,b; Williams et al., 2014; Kim et al., 2016, 2017; Moon et al., 2020) provide common testbeds for task-oriented dialogues. Following DSTC, researchers have created a variety of publicly available task-oriented dialogue datasets (El Asri et al., 2017; Shah et al., 2018; Budzianowski et al., 2018; Rastogi et al., 2020). Another line of work seeks to facilitate open-domain chatbot development with large amounts of human-created text data generated in a social context (Baumgartner et al., 2020) and supervision for a variety of desirable general qualities such as being engaging, personable, knowledgeable, and empathetic (Zhang et al., 2018; Dinan et al., 2019; Rashkin et al., 2019; Moon et al., 2019; Wang et al., 2019; Smith et al., 2020). Our work bridges the two lines. We compare ACCENTOR-SGD and ACCENTOR-MultiWOZ with relevant and representative dialogue datasets in Table 5.
Note that very few dialogue corpora contain explicit annotations for both task-oriented and chitchat utterances. For example, task-oriented dialogue corpora constructed by Rastogi et al. (2020) and Moon et al. (2020) contain annotations for a few chit-chat dialogue acts, but they are limited to light social greetings (e.g., "Thank you!", "Good Bye.") typically at the end of each dialogue session. Zhao et al. (2017) propose to artificially augment task-oriented dialogues with randomly sampled utterances from a chit-chat corpus, mainly to improve the out-of-domain recovery performance. Akasaki and Kaji (2017) annotate user utterances with chat/non-chat binary labels. Still, they do not study the contextual combination of these two
Dataset	Construction Method	# Dialogues	Task-Oriented	Chit-Chat
DSTC2 (Henderson et al., 2014a)	crowdsourcing	3,235	✓	✗
MultiWOZ 2.1 (Eric et al., 2020)	crowdsourcing	10,438	✓	✗
Schema-Guided Dialogue (Rastogi et al., 2020)	crowdsourcing	22,825	✓	✗
SIMMC (Moon et al., 2020)	crowdsourcing	12,948	✓	✗
PersonaChat (Zhang et al., 2018)	crowdsourcing	10,907	✗	✓
Wizard of Wikipedia (Dinan et al., 2019)	crowdsourcing	22,311	✗	✓
EmpatheticDialogues (Rashkin et al., 2019)	crowdsourcing	24,850	✗	✓
BlendedSkillTalk (Smith et al., 2020)	crowdsourcing	6,808	✗	✓
Pushshift Reddit (Baumgartner et al., 2020)	crawling & scraping	651,778,198†	✗	✓
ACCENTOR-SGD (this work)	crowdsourcing	22,825	✓	✓
ACCENTOR-MultiWOZ (this work)	crowdsourcing	997	✓	✓
Table 5: Statistics of dialogue datasets ( $\dagger$ : regarding each thread (i.e., a post and its comments) as a dialogue).
to make conversations more engaging, and their corpus does not contain goal labels like typical task-oriented dialogue corpora. In contrast, our work drastically increases the diversity and contextual coverage of chit-chat additions for any task-oriented dialogue corpus (e.g., "It's a great way to kick off the summer!", "I hear it's beautiful").
Compared with other approaches of creating a high-quality dialogue corpus (e.g., via human-to-human "Wizard-of-Oz" collection (Eric et al., 2020), dialogue self-play and paraphrase (Shah et al., 2018)), the annotation cost of the proposed model-based dialogue generation approach combined with the quality control mechanisms is lower, as our work does not involve authoring new sentences by human annotators.
5.2 Task-Oriented Dialogue Systems
Over the past few years, neural models have achieved remarkable success in the development of the main components of task-oriented dialogue systems, including understanding user intent, tracking dialogue states, determining system actions, and generating system responses (Henderson et al., 2013; Sun et al., 2014; Wen et al., 2015; Liu and Lane, 2016; Mrkšić et al., 2017; Wen et al., 2017; Nouri and Hosseini-Asl, 2018; Heck et al., 2020; Chen et al., 2020). Recently, connecting separate components and building end-to-end task-oriented neural dialogue systems have attracted increasing interest (Bordes et al., 2017; Peng et al., 2020b). The most recent thread is to unify all components in a single end-to-end neural model by fine-tuning a pre-trained deep language model on multiple tasks, which leads to state-of-the-art performance (Hosseini-Asl et al., 2020; Peng et al., 2020a). We follow this thread and further en
hance the ability to generate appropriate non-task-oriented add-ons, on top of the ability to achieve functional goals that existing systems are typically narrowly tailored to. A few work have studied training a dialogue model leveraging multiple chit-chat and task-oriented dialogues (Madotto et al., 2019, 2020), which allows the model to attend on a relevant task for a given user utterance and respond accordingly, thus increasing the skill coverage of the model. Our proposed models are trained on the newly collected ACCENTOR-SGD dataset with the turn-level supervision signals, allowing for contextual and flexible code-switching between chit-chat and functional tasks in a single system turn.
We propose adding chit-chat to enhance task-oriented dialogues (ACCENTOR) in this study. We present a general Human  $\leftrightarrow$  AI collaborative data construction approach for ACCENTOR, with which we create a dataset consisting of 23.8K chit-chat augmented task-oriented dialogues. We show via human evaluation that chit-chat augmented dialogues are preferred than the unaugmented. In addition, we propose three models for ACCENTOR. Evaluation results show that compared with the baseline trained on the original unaugmented data, our proposed models trained on the chit-chat augmented counterpart achieve a similar task performance level and higher human evaluation scores.
We thank Gerald Demeunynck for helping with the data annotation process. We would also like to thank the anonymous NAACL reviewers for their constructive and insightful feedback.
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A.1 Details of Candidate Generation