SlowGuess/NAACL_2022_OCR huggingface dataset:
Figure 3: An example to demonstrate how a conversation in DailyDialog can be re-purposed for the task of target-guided response generation.
POSITIVE
Gold c,r,t	CONTEXT c
RESPONSE r
TARGET t	the restaurant looks authentic
european.
the chef trained in florence.
the pasta tastes nice here.
NEGATIVE
Random t' with gold r,c	TARGET t'	i love to drive my car.
NEGATIVE
Random c' with gold r,t	CONTEXT c'	i enjoy computers and phones.
NEGATIVE
Random r' with gold c,t	RESPONSE r'	there is no parking here.
Figure 4: We train a reference-less model-based metric TARGET-COHERENCE to score the smoothness of a generate response wrt to dialogue context and target sentence. To train the metric, we synthesize hard negative examples using an ensemble of techniques as shown in this figure.
Dataset	Train	Dev	Test
Otters-id	1,929 (693)	1,160 (404)	1,158 (303)
Otters-ood	2,034 (677)	1,152 (372)	1,130 (372)
DailyDialog	11,118	1,000	1,000
Table 1: Overview of the datasets.
We use the gold transition response from the training dataset to create positive instances for training. For a positive instance with context  $c$ , target  $t$  and response  $r$ , we create negative instances using the following mechanisms: 1) We hold two out of (c,t,r) constant while randomly sample the third one. For example, sample a random context  $c'$ , which makes  $r$  incoherent to the  $c'$ . An example is shown in Figure 4. 2) We use a GPT-2 model trained on Otters dataset to generate a response  $r'$  coherent to  $c$  but conditioned on a random target  $t'$ . 3) For a target  $t$ , we chose a response  $r'$  from the Otters training set which has  $t$  as the target but context  $c' \neq c$ . We sample a maximum of 2 negative instances per mechanism and balance the count of positive and negative instances by repeating positive instances. We fine-tune a pre-trained BERT-base (Devlin et al., 2019) model on these instances with binary cross entropy loss.
7.1 Datasets
We use two datasets in our experiments. 1) Otters (Sevegnani et al., 2021) contains instances with context-target-transition response triplets. It consists of two sets of splits. The Out-Of-Domain (OOD) split ensures that none of the context-target pairs in the test set are present in the train set. In the In-Domain (ID) split, one of either the context or the target in each pair in the test-set is allowed to appear in the train-set. DailyDialog dataset con
sists of casual conversations between two speakers. In Table 1 we present the number of dialogues in DailyDialog dataset and number of responses in otters, along with number of unique context-target pairs in brackets. Otters dataset consists of multiple responses per context-target pair. Some transition responses in Otters dataset are noisy - they contain sentences and phrases from the target sentences. We remove such data from the test sets (with word overlap 0.75), leaving 1019 data points in the Otters-id test set and 988 data points in the Ottersood test set.
7.2 Baselines for generation
We report results for a number of baselines. We provide complete implementation details of CODA and all baselines in Appendix A and B.
- GPT-2: (Radford et al., 2019) A pretrained GPT-small language model fine-tuned on Otters data. Conditions on the context and target sentences to generate the transition response.
- GPT2-Fudge Yang and Klein (2021) uses a discriminator trained to distinguish good response continuations from the poor ones and guides the GPT-2 based decoder towards responses that are coherent to both the source and target sentences.
- Multigen (Ji et al., 2020) combines the vocabulary distribution generated by underlying GPT-2 model with a concept distribution from a commonsense knowledge base (ConceptNet).
- Concept-Predict leverages a concept prediction strategy from Qin et al. (2020a). The concept is predicted based on closeness to the target.
- CS-Pretrain model is pretrained with commonsense paths used for training the KPG models and is based on the commonsense story generation model from Guan et al. (2020).
Ablation experiments: We report results for following CODA variants:
- CODA-ONLYDA: CODA variant that uses DailyDialog augmentation and does not use commonsense paths from KPG models in the CRG model.
- CODA-NoDA: CODA trained without additional data from DailyDialog.
- CODA-NoEDGE CODA variant that uses only entities and no edges from the path.
- CODA-NoALIGN: variant that relies on only KPG-ft for training and inference. Does not select paths based on alignment with responses.
- CODA-KBPATH: variant that retrieves paths
Metric	Target as Context as Reference	Correlation w ratings
response	response	response
BLEU	15.0	9.9	6.5	-0.11
METEOR	14.0	12.6	13.2	0.01
ROUGE-L	32.3	29.8	26.5	-0.04
BS-rec	38.1	38.9	41.3	0.05
BS-F1	42.8	42.6	38.9	-0.06
TARGET-COHERENCE	10.7	4.0	77.4	0.47
Table 2: We present the metric scores when using the target, context and one of the references as the response. All metrics except for TARGET-COHERENCE score the target and context higher than the reference. TARGET-COHERENCE achieves high correlation with human ratings. Underlined values represent statistically significant result with p-value  $< {0.05}$  .
directly from ConceptNet using the algorithm proposed in Lin et al. (2019).
- CODA-Upper Upper bound for CODA which uses paths inferred from the gold responses using the KPG-wc keywords model during inference.
7.3 Evaluation Metrics
We report standard automated metrics such as BLEU (Papineni et al., 2002), ROUGE-L (Lin, 2004), METEOR (Banerjee and Lavie, 2005), and BertScore (BS-rec and BS-F1) (Zhang et al., 2020). Evaluation is carried out using multiple references from the test set. Word-overlap metrics do not correlate well with human judgements (Liu et al., 2016). Additionally, we observe that on this task, even a poor transition response can get a high score on reference-based metrics if it has high overlap with the context or the target. We carry out an experiment where we use the target, context and one of the references as the transition response. An ideal metric would score the reference response high, and give low scores to target and context used as a response. In Table 2, reference-based metrics assign higher scores to target and context sentences used as responses compared to human-written responses. In contrast, TARGET-COHERENCE assigns high scores to reference responses and low scores to target and context sentences.
Correlation of metrics with human judgements: We investigate how well do the metrics correlate with human ratings of system outputs. To perform this analysis, responses from CODA, baselines, as well as reference responses are judged by crowdsource annotators who rate the smoothness of a response given the dialogue context and the target on a scale of 0 to 1. We collect a total of 440 ratings across Otters ID and OOD splits, and report Spearman rank correlation (Spearman, 1961) of the metrics and the ratings. Krippendorff's alpha for
In-Domain	Out-Of-Domain
BLEU	METEOR	ROUGE-L	BS-rec	TC	BLEU	METEOR	ROUGE-L	BS-rec	TC
GPT-2	3.4	11.9	23.9	35.4	26.7	3.0	10.8	22.2	35.0	29.7
GPT2-Fudge	3.4	12.4	24.4	36.1	28.3	3.4	11.1	23.0	35.1	29.6
Multigen	6.2	12.5	28.1	40.0	27.8	4.9	11.6	26.0	36.7	30.8
Concept-predict	3.3	12.3	28.5	38.1	28.3	3.7	11.6	23.1	35.9	26.3
CS-Pretrain	2.8	11.1	23.2	35.2	21.5	2.8	10.2	21.2	33.0	22.0
CODA	5.0	12.6	25.9	38.0	36.7	4.6	11.5	24.3	35.5	37.9
CODA-ONLYDA	4.0	12.4	24.4	37.5	32.7	3.1	11.1	22.7	35.3	33.2
CODA-NoDA	4.4	12.3	25.1	37.8	35.7	4.5	11.6	24.4	35.4	36.0
CODA-NoEDGE	4.2	12.0	25.0	37.4	33.7	4.0	11.8	24.2	35.4	35.9
CODA-NOALIGN	3.7	12.4	25.5	38.5	32.1	3.2	11.2	22.8	35.6	31.2
CODA-KBPATH	3.6	12.5	24.9	38.6	33.9	3.6	11.4	24.1	35.9	33.0
CODA-UPPER	8.3	18.1	32.6	44.4	47.9	7.5	17.9	30.7	42.7	45.4
Human	6.5	13.1	26.5	41.3	77.4	4.9	12.3	24.0	37.6	77.3
annotation is 0.42. Results, shown in last column of Table 2, depict that most standard automated metrics correlate poorly with human ratings, while the, proposed TARGET-COHERENCE achieves a very high correlation score of 0.47.
We present the Amazon Mechanical Turk interface for human ratings collection in Figure 5 in the Appendix. The workers were first shown instructions about the task with definitions and examples for all the rating criteria. We paid crowd workers on Amazon's Mechanical Turk platform  $0.7 per annotation and gave bonuses to annotators with high annotation quality. Our estimated hourly pay was$ 13, which is above the minimum US federal hourly wage. We set the worker qualification condition as 1000 HITS completed, 95% or more approval rate and location as native English speaking countries. We release the human ratings and system outputs used for computing the metric correlations as part of this work.
7.4 Results
In this section we present the automatic and human evaluation results. Automated metric results are summarized in Table 3. Although reference-based metrics are lexically biased (subsection 7.3), we still report their scores. We observe that CODA outperforms all the baselines under in-domain (ID) as well as out-of-domain (OOD) setups of Otters data as per TARGET-COHERENCE (TC) score. For example, CODA gets a high TC score of 36.7 (ID) and 37.9 (OOD) while the TC scores of the closest baselines GPT2-Fudge, Multigen and Concept-predict are in the range of 28-31, demonstrating that the proposed method leads to significant improvements in response quality. However, CODA is far from reaching human performance (TC 77.4).
CODA Ablations: We observe that: (1) Not us
Table 3: We present the results of automatic evaluation based on word-overlap and proposed TARGET-COHERENCE. CODA outperforms all the baselines for most of the metrics. We also present results for CODA's model ablations.
Criteria	Models	Win	Lose	Tie
Smooth	CODA vs GPT-2	37.5	31.6	31.0
CODA vs Multigen	32.3	22.8	44.8
Sensible	CODA vs GPT-2	22.0	21.3	56.7
CODA vs Multigen	25.8	25.6	48.6
Informative	CODA vs GPT-2	32.3	27.3	40.4
CODA vs Multigen	35.5	27.8	36.7
Table 4: Human evaluation through pairwise comparison between CODA and baselines. CODA is preferred in smoothness and informativeness criteria while being comparably sensible.
ing commonsense knowledge (CODA-ONLYDA) leads to large performance drops, highlighting that CODA effectively utilizes commonsense knowledge. (2) Dropping data augmentation leads to a small drop in performance (CODA-NoDA), hinting at relatively small (but still significant) benefit from pretraining the model using data augmentation. (3) Low performance of CODA-NoEDGE shows the importance of using edges in commonsense paths. (4) Not aligning and selecting paths based on their relevance to responses during CRG training (CODA-NoALIGN) leads to a high drop in performance. (5) CODA outperforms CODA-KBPATH by  $8\%$  (ID) and  $14.5\%$  (OOD). This improved performance can be attributed to the generalizability of entities and paths generated from the KPG models. (6) CODA-UPPER achieves high scores, highlighting that further improvement in commonsense path generation component can significantly boost the output quality of CODA.
Human Evaluation: We conduct human evaluations on Amazon Mechanical Turk to evaluate the quality of generated transition responses. Annotators are requested to evaluate the transition response on following criteria: (1) Smooth: rate whether the response serves as a smooth transition between the dialogue context and target. (2) Sensible: whether the response makes sense in itself i.e.
Context: i like the sand on my feet
Target: my puppy is called georgie.
GPT-2: My mom likes the water.
Multigen: My pet is the gecko.
CODA: My dog walks along the beach with sand.
CODA-Path: sand is at location beach belongs to walk is desired by puppy
Context: my favorite city is seattle.
Target: i ride my bicycle everywhere.
GPT2: Seattle is my favorite city to go to
Multigen: So what do you do when you go to the seattle
CODA: I bought my bicycle from a bike shop in seattle.
CODA-Path: favorite city is the location which has bicycle shop is a dependency of ride bicycle
Context: i am a server at a food place.
Target: i eat greasy foods.
GPT-2: I eat healthy foods at restaurants.
Multigen: I hate my food.
CODA: I am a server, but I don’t want to eat too much.
CODA-Path: server is a person not desires eat greasy food
Table 5: Sample representative model outputs. The knowledge paths used by CODA provide interpretability and control over the response generation process
it is grammatical and logically coherent. (3) Informative: how much informative content a response carries. Human annotators compare (or mark as a tie) responses from two models. We collect two annotations for 100 randomly selected data points from the test outputs. Results in Table 4 demonstrate that CODA outputs are preferred over the baselines on 'Smooth' and 'Informative' criteria.
We present representative outputs from the models in Table 5. For CODA, we show the path used in response generation. We notice that GPT-2 and Multigen often tend to either generate simple outputs (e.g. 'I hate my food' in the last example) or simply repeat or address either the target or the context (e.g. 'My pet is the gecko', 'Seattle is my favorite city to go.') which leads to high BLUE and METEOR scores, but low TC scores. CODA avoids these pitfalls as it is conditioned on generated commonsense paths based on both the context and target entities leading to more informative and sensible outputs. However, CODA is susceptible to two issues: 1) Using poor keywords for path generation, and 2) Generation of irrelevant paths (e.g. 'server is a person not desires greasy food' in the last example).
Path quality: We conduct a human evaluation study to measure the quality of the generated paths. For randomly selected 100 generated responses, we ask annotators to judge 1) Relevance: Is the path relevant and used in the response? and 2) Makes
sense: Does the path makes sense? Results reveal that  $79\%$  of the paths were judged to be relevant and  $76\%$  of the paths were judged to make sense. Thus in aggregate, the generated knowledge is good in quality, and is used in the generated response. Path novelty: We analyzed the paths generated by CODA which were judged as sensible by human annotators and found that  $26.8\%$  of entities in the paths were not found in ConceptNet. This include entities such as 'favorite food', 'pet kitten', 'single kid' and 'online class'. Thus, the actual paths from the ConceptNet might not be able to cover a large fraction of head/tail entities. Furthermore,  $81\%$  of sensible paths are novel and do not exist in ConceptNet. For example, even though the path 'eat motivates go to restaurant has subevent dinner is the location for bread' exist in ConceptNet, the path 'eat motivates go to restaurant has subevent dinner is the location for pizza' does not exist in ConceptNet. Thus we show that CODA can generalize to new entities and paths.
In Appendix C we discuss a human-in-the-loop study for controllability. The human-in-the-loop experiment shows that even minimal human intervention in the form of domain relevant keywords input for knowledge paths can improve the quality and smoothness of the transition responses.
In this work, we propose and evaluate models for target-guided response generation using explicit commonsense bridging paths. We also introduce an automated metric to evaluate smoothness of a transition response. We showed that our model generates more smooth and informative outputs through automatic and human evaluation. Furthermore, it allows for more interpretable results. Going forward, we envision a model which could combine target and non-target guided dialogue planning.
We thank Maarten Sap and the anonymous reviewers for providing valuable feedback. This work was funded by the Defense Advanced Research Planning Agency (DARPA) under DARPA Grant N6600198-18908, and the National Science Foundation under Award No. IIS1816012. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.
Broader Impact and applications: Our proposed models for target-guided response generation can be used to generate responses based on target sentences that can drive the system's agenda in a conversation. Deploying a target-sentence guided dialogue model needs careful consideration and testing since designating a target sentence for all turns of a conversation might disrupt the natural flow of the conversation. Therefore, they can be deployed alongside existing non-target guided dialogue models that perform free-flow conversations without predesignated targets. At each turn of a conversation, a central system can use the target-coherence metric to decide if the system should generate a target-guided response or a simple follow-up response to the context. Target-guided systems can be used for several useful applications such as creating non-obtrusive recommendations, comforting people, recommending new products and services, and introducing interesting new topics and educating users about those topics.
Potential risks and solutions: We wish to raise awareness about potential misuse of proposed systems for persuading users by people with ill intentions. For example, conversational systems can pose as humans and then proactively alter user's perceptions about specific issues, evaluations of products or services, or political inclinations. To circumvent such issues, it is necessary to improve transparency through regulations, such as informing the users that they are conversing with a bot and not a human. Regulations are necessary to avoid hazardous outcomes during deployment for specific domains. For example, European Union's regulatory framework proposal on artificial intelligence<sup>2</sup> defines use of AI systems for "educational or vocational training, that may determine the access to education and professional course of someone's life" as high risk. Anyone who uses or builds upon our system should comply with such regulations. Apart from regulations, recent safety and ethics related research and datasets (Baheti et al., 2021; Sun et al., 2021) in conversational AI can help in mitigating aforementioned issues. Henderson et al. (2018) and Dinan et al. (2021) highlight and discuss potential ethical and safety issues that arise in dialogue systems research. Xu et al. (2020) provides a review of recent methods that try to mitigate
safety issues in open-domain dialogue generation which can be utilized for the target-guided response generation task.
Limitations and potential biases: Current conversational systems suffer from several limitations, such as, they are not good at human qualities such as empathy, morality, discretion and factual correctness. There is a risk that a target driven system would ignore these factors to achieve the target. Therefore more research is needed to equip bots with such qualities. Our models are trained on existing datasets such as Otters and DailyDialog, and also leverage external commonsense knowledge resources. Knowledge graphs such as ConceptNet have been found to contain biases and have weak representations of moral common sense knowledge (Hulpus et al., 2020; Mehrabi et al., 2021). While grounding on knowledge paths from knowledge graphs can provide insights and explanations about the model's reasoning, our models could potentially inherit biases present in these data sources. Advancements in adding a moral dimension to KGs, and extending them with intuition of morality (such as crime is bad), can enable generation of morally correct knowledge paths. Furthermore, imbuing conversational systems with empathy (Ma et al., 2020), moral discretion (Ziems et al., 2022) and factual correctness (Gupta et al., 2021b; Dziri et al., 2022) will improve users' experience and trust in the system.
We have included the Mechanical Turk arrangements and worker pay in the last paragraph of the section 7.3. We paid well above the US federal minimum wage (around $13 hourly) and provided enough time to the workers to complete the task which was determined based on a few pilot experiments.
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