SlowGuess/NAACL_2022_OCR huggingface dataset:
Can human involvement improve generation? Our CRG model uses explicit paths generated from the KPG models, which not only provides interpretability, it also allows human-in-the-loop intervention for finer controllability. To test this hypothesis, we create a model KPG-oneent which is a hybrid version of KPG-wc and KPG-ht model. The model takes a single entity  $n_k$  given by a user as an input and is trained to generate a path containing that entity. We test this model on a manually created set of target sentences  $S$  of size 10 belonging
Context: i dye my hair.
Target: we should donate to charity.
Path (KPG-oneent): hair belongs to people motivated by give assistance has requisite donate to charity.
CODA-controlled: I donate my hair to a non-profit that helps people in need.
Path (KPG-ht): hair belongs to people desires donate to charity
CODA: People who donate are very good people.
Context: i have an amazing garden.
Target: you can try our restaurant.
Path (KPG-oneent): garden is a location of grow food motivated by goal best ingredients is desired by person capable of try restaurant
CODA-controlled: My restaurant uses the best ingredients from the garden.
Path (KPG-ht): garden is a location of have friends over has prerequisite try restaurant
CODA: you can have friends over.
Table 8: Sample data and model outputs from the human-in-the-loop experiment. The underlined words are keyword inputs provided to the model KPG-oneent. The italicised words in the CODA controlled outputs are phrases are generated based on the input keywords.
to domains such as healthcare and charity. The data created is shown in Table 7. An example sentence in set  $S$  is 'we should donate to charity' and we manually curate a set of keywords such as 'help poor', 'give assistance' and 'tax deductions' that are relevant to the target sentence of interest and can guide the knowledge path sampling towards meaningful paths. This data creation took the authors 30 minutes of effort. For 100 random sampled contexts from the Otters dataset, we select a random target sentence from the set  $S$  and sample a
keyword  $k$  from the curated set of keywords of that target. We compare this controllable model with the KPG-ht model that was used for path generation in all our experiments. We present sample outputs of the model in Table 8. The input keywords used as intervention are underlined. The paths which use the keyword intervention generate smoother transitions compared to the paths which do not use the keyword intervention. We find that the TARGET-COHERENCE metric favors the KPG-oneent model in 59 percent of cases, confirming that even minimal human intervention in the form of domain relevant keywords can improve the quality of generation.