SlowGuess/NAACL_2021_OCR huggingface dataset:
Relevance: The reference sentence may not be the only correct sentence that fits the context. This is especially true in dialogue generation tasks where contexts like "How are you?" and "What was your favourite part of the movie?" can have many correct responses that can be produced by grounding on the same document. Hence, we measures whether the generated output contained salient information from the document written in a manner appropriate to the context. The annotators are provided with the document  $\mathbf{d}_i$ , the context  $\mathbf{c}_i$ , and the outputs of the two systems  $A$  and  $B$ , again in a random order. They were instructed to "Pick the option which contains information from the document and fits the dialogue context coherently". Note that the annotators don't have access to the reference in this evaluation. Each judge had to consider whether the information fits with the context and also whether system-generated content could be supported by the document.
Fluency: Finally, we evaluate the fluency of the generated sentences on a scale of 1 (unreadable) to 4 (perfect) as is described in (Zhou et al., 2018).
Human evaluation was conducted on Amazon Mechanical Turk. We conduct 3 comparative studies between the BART, CoDR and DoHA outputs. Each worker was asked to annotate 10 pairs of sentences. We added one control pair among them i.e for  $1/10$  pairs, both the sentences were exactly the same. If a worker provides wrong judgement for the control pair then their annotations were
discarded. For each dataset we have total 540 comparative judgements and 90 sentences of each of the models marked for fluency.
Table 2 shows the results of the human evaluation on closeness and relevance. The closeness results show that all the three models BART, CoDR and DoHA generate sentences that are close to the reference, although CoDR and DoHA outperform BART in most cases. Interestingly, the relevance results for Wikipedia Update Generation and CMU_DOG datasets show that CoDR and DoHA generate content that is grounded in the document as opposed to BART. BART baseline generates sentences that are fluent and close to the reference but does not ground in the content of the document as compared to CoDR and DoHA. The 'No Preference' is generally opted over any of the models which is further discussed in §6. For the relevance comparison, annotators have to read a large document to figure out if the generated information is present in the document or not. This can make the annotations noisy especially for Wizard of Wikipedia dataset which has 7 passages as grounding document.
Since both CoDR and DoHA are also BART-based models, the fluency for all three of them is very high and close to each other (BART=3.64, CoDR=3.71, DoHA=3.66).
CoDR and DoHA: The DoHA model still uses the content driven representations  $(\mathbf{h}_d$  and  $\mathbf{h}_c)$ . The main difference is that in CoDR model we concatenate  $\mathbf{h}_d$  and  $\mathbf{h}_c$  and pass it to the decoder but for DoHA we pass  $\mathbf{h}_d$  and  $\mathbf{h}_c$  separately to the decoder. DoHA has an additional attention layer to focus on the representation of the document  $\mathbf{h}_d$
Error Class	%	Chat context	Reference	Generation
Reference and genera-tion are grounded	35	the story is sounding even more interesting. who do you think had the most growth or development in the film?	but there's a twist in that anna has been deceived by the man she was in love with, he was really plotting to take over the kingdom from the sisters the whole time.	but there's a twist in thatanna is actually in love with a man who is actually a bad guy. he was actually trying to take over the kingdom from the sisters after Elsa injured anna and then their parents died.
Reference is grounded but generation is not	3	hello!	have you heard about the post directed by spiel-berg?	hello!
Reference is not grounded but genera-tion is	5	yes i agree and how meryl can depict the first female pub-lisher. that would be interest-ing to see the dynamic of how it effected her career as well	yes, for a woman to have a successful career in those times was not an easy task, for sure.	definitely. i wonder how mcna-mara felt when he saw the docu-ments and then told ellsberg that the war was hopeless.
Reference and genera-tion are not grounded	43	the shark was so realistic	yea i live near a beach and it made me not ever want to go swimming	i never thought of it that way, and i agree, it made me terrified to go into the ocean for awhile.
Table 3: Error analysis for the CMU_DoG dataset (only shown for outputs with low Rouge-L score). Text in green indicates grounded information.
only. In this loose sense, DoHA is CoDR plus additional parameters in attention layer to focus on  $\mathbf{h}_d$ . DoHA performs marginally better than CoDR in automated metrics. But qualitatively (human evaluation) DoHA produces higher quality outputs as compared to CoDR. Table 2 shows DoHA performing better than CoDR on all but one case.
We manually inspect the outputs of the CoDR model on the development set of CMU_DoG and Wikipedia Update Generation dataset to understand the their quality. We inspect 60 samples in each dataset which have Rouge-L score  $< 60$ . These are chosen such that we have 10 samples in each of the 6 buckets of Rouge-L score ( buckets are range of 10 points: 0-9, 10-19, 20-29, 30-39, 40-49 and 50-59). We analyse the generated outputs along the two aspects of appropriateness of the generation to the context and its grounding in the document.
CMU_DOG: We find that 52/60 (86.7%) responses were appropriate to the given chat context. These 52 responses are further categorized in Table 3. We found that for about  $90\%$  of samples, if the reference is grounded then the generation is also grounded and if the reference is not grounded then the generation is not grounded. Further inspection shows that references are not grounded if they are follow up questions, opinions or experiences that are shared in the conversation. In most of these cases, the context dictates if the response should be grounded or not grounded in the docu
ment. Since, all of the generated responses in this category are appropriate to the context suggests that these conversational subtleties are not captured by automated evaluation metrics and are given a low score. We also observe a few data artifacts like the mapping of the Wikipedia sections and the chat context is noisy for this dataset. This can be easily resolved by providing all the previous passages of the conversation as grounding to the model. We would also like to note that this dataset was collected under two scenarios: (1) both the people in the conversation have access to the document, and (2) only one person has access to the document. But this distinction is not made in modeling the task. The noise in the dataset can be reduced by modeling only the users that have access to the document in the conversation (similar to Wizard of Wikipedia where only the wizard is modeled).
Wikipedia Update Generation: The error analysis for this task is shown in Table 4. For  $5\%$  cases, the reference itself is not grounded in the document. The remaining  $95\%$  cases are further classified into 4 error categories. About  $85\%$  times, the generation is either completely or partially grounded if the reference is grounded.  $43\%$  generations are grounded in document but are linguistic variations of the reference or could be alternate updates to the context. Yet, these are scored low on the Rouge-L metric revealing the inadequacy of the automated metrics. For  $23\%$  cases the generation partially hallucinates some information or misses some information present in the reference.  $22\%$  times the
Error Class	%	Reference	Generation	R
Linguistic Variation: Reference and generation are grounded and generation is appropriate but a linguistic variation of the reference or an alternate appropriate update.	43	December 12 - The Smiths play Brixton Academy, their last ever gig before their dissolution.	December 12 - The Smiths per-form their final show, at Brixton Academy in London.	41
Partial Hallucination: Reference and generation are grounded but generation is either missing or hal-lucinates some information	23	America Online and Prodigy (online service) offered access to the World Wide Web system for the first time this year, releasing browsers that made it easily accessible to the general public.	The World Wide Web was first introduced on January 17, 1995 on Prodigy.	17
Incoherent Reference: The reference does not coherently follow the context	22	“The Naked Ape”, by Desmond Morris, is published.	Zoologist Desmond Morris publishes “The Naked Ape”.	26
Incorrect: The generation is either not appropriate or is not grounded (completely hallucinates the information).	7	The year 2000 is sometimes abbreviated as “Y2K” (the “Y” stands for “year”, and the “K” stands for “kilo-” which means “thousand”).	The Y2K conspiracy theory claimed that a secret nuclear attack by the United States on 2 January 2000 was planned to begin World War 2.	9
Reference is not grounded	5	This was achieved under dead calm conditions as an additional safety measure, whereas the Wrights flew in a 25 mph+ wind to achieve enough airspeed on their early attempts.	This was verified by a video crew present at the test flight.	14
Table 4: Error Analysis for Wikipedia Update Generation task (R denotes Rouge-L score. Text in red indicates hallucinated or missing information.)
reference itself does not seem to coherently fit the context. This is primarily observed for Wikipedia pages that are in the form of a list like 1340s and Timeline of DC Comics (1950s). Yet, for  $50\%$  of the Incoherent Reference cases, the generation is grounded in the document and very close to the reference (like the example in Table 4). Only for  $7\%$  of the cases, the generation is completely incorrect and hallucinates all of the information. Future work can focus on improving the error in the Incorrect and Partial Hallucination error classes.
Reference Comparison: With the insights from manual inspection, we performed another comparative study with human judges (on Amazon Mechanical Turk). This was to understand how our models perform in comparison with the reference. The judges are instructed to "Pick the option that is most appropriate to the given context". We annotated 100 samples for each DoHA and CoDR model in comparison with the reference on the CMU_DOG and Wikipedia Update Generation datasets. We perform two separate comparative experiments: Reference vs CoDR and Reference vs DoHA. The results in Table 5 show consolidated results for the two models. It shows the total number of times reference was selected, the total number of times 'No Pref' was selected or the total number of CoDR or DoHA was selected. It demonstrates that our mod
els produce appropriate outputs which can be used as alternate responses/updates. Our models are preferred over the reference in both the tasks suggesting that the automated evaluation is insufficient and the sole reference should not be considered as the only correct response to the context.
Generation grounded in document has been studied through a large body of summarization work (Rush et al., 2015; Nallapati et al., 2016) and similar tasks such as headline generation (Tan et al., 2017). Multiple new works have extended this research in new directions; Wikipedia Update Generation (Prabhumoye et al., 2019) introduces the task of generating an update to the Wikipedia context based on a news document; Wikipedia article generation (Liu et al., 2018) introduces the task of generating an entire Wikipedia article based on multiple documents; Text Editing by Command (Faltings et al., 2020) introduces the task of generating a particular type of Wikipedia edit conditioned on a command provided in natural language and a grounding consisting of snippets of 200 web page results.
Parallely, new tasks have also emerged focusing on document grounding for dialogue response generation (Zhou et al., 2018; Dinan et al., 2018). Zhao et al. (2020a) explore this task in low-resource set-
Dataset	Ref	NoPref	DoHA/CoDR
Wikipedia	33.9	28.3	37.8
CMU_DOG	22.8	45.6	31.6
Table 5: Comparison with reference (Ref) in %age
ting and use pre-training along with a disentangled decoder. The disentangled decoder consists of a context processor, knowledge processor and a language model. A dialogue manager is used to combine the vocabulary distributions provided by these three components. Zhao et al. (2020b) propose a knowledge selection module integrated with pre-trained language models for this task.
Cao et al. (2020) use pre-trained language model GPT-2 (Radford et al.) and explore various attention fusion techniques for persona-based dialogue generation (Zhang et al., 2018b; Dinan et al., 2020). Our DoHA technique also introduces an additional attention multi-head but does not use any additional weights to fuse attention heads. Similarly, Junczys-Dowmunt and Grundkiewicz (2018) use an additional attention multi-head in transformer architecture for automatic post-editing task. We demonstrate how attention can be enhanced in pre-trained models. The CoDR model fuses the representations of the document and the context in the decoder which is inspired by the fusion-in-decoder model in open-domain QA (Izacard and Grave, 2020). Although Bruyn et al. (2020) introduce the usage of BART for knowledge grounded dialogues, it is primarily from the perspective of improving knowledge retrieval. We provide benchmark BART numbers (Table 1) for the generation task. Prabhumoye et al. (2020) provide a schema containing five modules which can be changed to control the generation process. While Zhao et al. (2020a) modify the external input and the output module, we focus on the external input and the generator module of the pre-trained language model.
This paper proposes two novel improvements for document grounded generation and provides a stronger baseline. This paper demonstrates how similar modeling techniques could be used for two previously separately modeled tasks. Our proposed models outperform the previous techniques and the new stronger baseline on automated metrics and human evaluation for the three datasets discussed in the paper. We present a comprehensive manual inspection which reveal certain data artifacts and
provides us with insight on how to model these tasks in future. Particularly, future work can focus on designing better evaluation metrics which don't penalize linguistic variations in generation. Better models can also be constructed to focus on cases of partial hallucination or incorrect responses.
The intended use of the models proposed is to aid the NLG systems in generating content-rich text. Note that this does not imply that the models generate factually correct text. The generation entirely depends on the information in the document provided. If the document itself is factually incorrect then the generation would be grounded in false content and hence generate inaccurate text.
We hope that this technology is used for socially positive applications like building trust of users in dialogue systems like Alexa, Siri and Google Home by providing users with credible information. This work has specifically focused on two datasets of dialogue response generation with the aim that this research not only helps in generating responses which contain useful information but also increase credibility of responses by disclosing the source of information. If dialogue systems base their responses on certain sources of information then they can potentially disclose the source of information to the user. The user then has the agency to make informed decision about trusting the system responses or not.
Additional generations are shown in Appendix (§B). Table 8 and 9 in Appendix §B show the potential misuses of models trained on this task. For both the experiments, a few news articles were hand selected and relevant context was selected from a chosen Wikipedia article. In case of Table 9, the context was curated by hand. Interestingly, the tables also show the sensitivity of the trained model to the document information. It consists of the same context but different documents were provided as inputs to the model. The generated outputs are different for each document.
This work was supported in part by ONR Grant N000141812861 and NSF IIS1763562. We are grateful to Semih Yavuz and Caiming Xiong for valuable discussions at earlier stages of this work. We would like to thank Srinath Reddy Meadusani for his technical support throughout the project.
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