SlowGuess/NAACL_2021_OCR huggingface dataset:
We summarize model configurations in Table 6, which are employed together for candidate generation in Section 2.1. Our implementation is based on ParlAI (Miller et al., 2017), and all unspecified parameters take the default values set in the interactive mode of ParlAI.
Generative Model	Beam Size	Minimum Beam Length
BlenderBot (90M)	10	1
BlenderBot (90M)	10	5
BlenderBot (90M)	10	20
BlenderBot (90M)	30	20
BlenderBot (2.7B)	10	1
BlenderBot (2.7B)	10	5
BlenderBot (2.7B)	10	20
BlenderBot (2.7B)	30	20
BlenderBot (9.4B)	10	1
BlenderBot (9.4B)	10	20
GPT-2 (117M)	10	1
GPT-2 (345M)	1	1
GPT-2 (345M)	10	1
GPT-2 (345M)	10	5
GPT-2 (345M)	10	20
GPT-2 (345M)	30	1
GPT-2 (762M)	10	1
Table 6: Employed models and decoding parameters for candidate generation.
A.2 Details of Candidate Filtering
The ranker initially ranks each candidate according to the posterior probability output by the binary classifier. It then lowers the ranks of candidates that match a list of bad patterns. Most bad patterns are about newly introduced counterfeit information (e.g., containing an URL/email address, a phone number, time, or amount of money). The rest of the bad patterns are mainly about text genre (e.g., containing email sign-offs such as "best regards") and format (e.g., misuse of punctuation marks). Lastly, the ranker raises the ranks of (i) uncommon candidates and (ii) candidates that are dissimilar to the other candidates for the dialogue and the system response being augmented. We measure the similarity by Levenshtein distance.
Note that we do not explore the optimal settings for candidate filtering, as it is not the primary focus of this paper. For instance, how much the rule-based ranker lowers or raises the ranks of candidates is set manually based on engineering intuition rather than rigorous analysis; we do not exhaustively investigate how much labeled data is required to obtain a good enough binary classifier; the 1.7K examples from the pilot annotation are randomly sampled. Tuning the procedure (e.g., the
number and selection of training examples) may lead to a better resulting candidate set.
A.3 Human Evaluation Questions
- Engaging: Who would you prefer to talk to? Which version is more likely to hold your attention and make you want to hear more?
- Interesting: Who would you say is more interesting? Which version arouses your curiosity or tells you something new or useful?
- Humanlike: Who would you say sounds more human? Which version is more natural and personable?
- Knowledgeable: Who would you say is more knowledgeable? Which version seems more well informed and confident in the information?
A.5 Generation Samples
Table 8: Sampled responses generated for a complete task-oriented dialogue (U: user, ST: SimpleTOD, S+: SimpleTOD+, AR: Arranger, RW: Rewriter).
As shown in Table 8, we observe that compared with SimpleTOD+, both Arranger and Rewriter tend to add chit-chat to the beginning of task-oriented responses. This is perhaps because the underlying off-the-shelf chit-chat model takes only  $u_{1}, s_{1}, \ldots, u_{i}$  as input, making it more likely to generate a suitable chit-chat to start, rather than end the  $i$ -th system turn. The responses generated by Arranger and Rewriter are similar because Rewriter generates responses by copying contents from the responses output by the underlying off-the-shelf models without modification for most of the time (87.0% of dialogues on the test set).