On the Underspecification of Situations in Open-domain Conversational Datasets
Year: 2023

Abstract:
Advances of open-domain conversational systems have been achieved through the creation of numerous conversation datasets. However, many of the commonly used datasets contain little or no information about the conversational situation, such as relevant objects/people, their properties, and relationships. This absence leads to underspecification of the problem space and typically results in undesired dialogue system behavior. This position paper discusses the current state of the field associated with processing situational information. An analysis of response generation using three datasets shows that explicitly provided situational information can improve the coherence and specificity of generated responses, but further experiments reveal that generation systems can be misled by irrelevant information. Our conclusions from this evaluation provide insights into the problem and directions for future research.

heading: 1 Introduction
text: Human communication heavily relies on information about a situation–a set of observable information about relevant objects, their properties, and relationships in the world at a specific time and place. Crucial information can often be implicitly assumed during a conversation, particularly when it can be inferred from co-observed facts (e.g., “they are talking at the front desk in a hotel.”) or shared a priori (e.g., “the person was staying in the hotel”).
Incorporating such information is essential for both the development and evaluation of conversational technologies. To illustrate this point, we show a conversation example from DailyDialog (Li et al., 2017), along with responses generated by three systems: BlenderBot2 (Komeili et al., 2022; Xu et al., 2022), GPT-3 (Brown et al., 2020), and
∗This work was done while the first author was at Carnegie Mellon University.

heading: No situational information in input
text: ChatGPT (GPT-3.5) in Table 1.1 DailyDialog is a widely used2 dataset of multi-turn conversations in English. The original example does not describe a surrounding environment explicitly, resulting in ambiguity regarding the situation. Person A could be a traveler leaving a hotel or someone handing over their house key, among other possibilities. The response generated by BlenderBot2 is somewhat relevant to the latter situation but clearly inappropriate in the former. In contrast, the response generated by GPT-3 is appropriate in the former situation but not in other contexts. ChatGPT’s response is neutral, though less engaging. This ambiguity underscores the fundamental problem caused by
1See Appendix A.2 for the generation setup. 2Based on Semantic Scholar, the dataset paper (Li et al.,
2017) is cited by over 700 papers as of April 2023.
12
the underspecification of the situation. The provision of situational information, such as “they are in a hotel,” narrows down the range of ideal behaviors, which helps generation systems produce context-specific responses and establishes a more solid standard for judging quality. This issue is not limited to this particular dataset. Many common open-domain conversational datasets contain little or no additional information besides conversation history (the Twitter dataset (Ritter et al., 2011); DREAM (Sun et al., 2019); MuTual (Cui et al., 2020); inter alia). This task setting, which requires systems to infer almost all information solely from previous utterances, poses unnecessary challenges and may lead to undesired system behavior.
In this position paper, we discuss the current state of open-domain conversational datasets concerning how situations are represented (§2). Specifically, we consider situational statements3 that provide partial information about immediately observable (e.g., today’s weather), commonly known (e.g., umbrellas are often used on rainy days), or directly derivable facts related to the task, speaker, and goals (e.g., the hotel’s check-out and a guest’s required action). Some of these elements have already been effectively integrated into modern conversational systems, particularly for closeddomain, task-oriented dialogues. We argue that open-domain conversational tasks and datasets should be equipped with some form of situational information. Additionally, we conducted case studies on several datasets to explore the potential benefits and challenges associated with situational information (§3). Our analysis indicates that distinguishing between relevant and irrelevant situational information can be challenging for data-driven response engines, offering opportunities for future research.

heading: 2 Status Quo
text: In open-domain response generation tasks, systems generate responses in natural language based on input dialog history (a list of utterances from previous turns). Dialog history often serves as the primary, and sometimes sole, source of context information in many datasets. In this section, we discuss how conventional task design can be improved through the explicit inclusion of situational information.
3The situation of a conversation consists of numerous predicates that describe various aspects of surroundings. By a situational statement, we mean a single predicate that describes part of a situation.

heading: 2.1 Open-domain Conversational Datasets
text: The recent advancement of open-domain conversational technologies can be largely attributed to the development of large-scale conversation datasets, which facilitate the training of data-driven language generation models. However, many commonly used datasets lack crucial situational information. Below, we provide a brief overview of representative datasets in the field.4
Collection of naturally occuering conversation data can be costly (Godfrey et al., 1992). This bottleneck was greatly alleviated by public web resources that contain naturalistic textual conversations. For instance, millions of conversations can be scraped automatically from Twitter (Ritter et al., 2010). Likewise, many large-scale datasets were produced from social media (Wang et al., 2013; Sordoni et al., 2015; Shang et al., 2015; Henderson et al., 2019). While conversations on social media are essentially text chat and do not cover many of the dailylife interactions, online language learning coursewares contain conversation examples in diverse scenarios (Li et al., 2017; Sun et al., 2019; Cui et al., 2020). DailyDialog (Li et al., 2017) is one of the datasets built from English learning materials and 13k multi-turn conversationswe spanning various topics and scenarios. These (semi-)automatically harvested datasets are generally large and effectively used for pre-training language models (Humeau et al., 2019; Shuster et al., 2020). However, they contain only conversation history.
Some prior studies have created conversational datasets enriched with various semantic and pragmatic features. Notably, multi-modal and taskoriented datasets generally allocate dedicated representations for essential situational information such as physical signals (Haber et al., 2019; Moon et al., 2020) and task-specific information or domain knowledge (Budzianowski et al., 2018), but their coverage is limited to one or a few specialized domains. For open-domain conversation systems, the use of focused information has been explored for improving response quality, such as related documents (Zhou et al., 2018; Dinan et al., 2019) and user-based features such as persona (Zhang et al., 2018; Majumder et al., 2020; Dinan et al., 2020b), emotion (Rashkin et al., 2019), social norms (Kim
4For a more comprehensive literature review, refer to survey papers on available resources (Serban et al., 2017; Kann et al., 2022).
et al., 2022), and behavior (Ghosal et al., 2022; Zhou et al., 2022). Sato et al. (2017) explored the utilization of time information as well as user types for analyzing conversations on Twitter. Though these studies demonstrate that integrating surrounding information improves response quality in various aspects such as informativeness and engagement, the scope has been limited to specific modalities, domains, and semantic categories. Moreover, detecting certain features, like internal emotion and plans, can be non-trivial in practice. Observable situational information has received little attention. Otani et al. (2023) aimed to represent such information in free-form English texts, but the available resources are limited, and it remains unclear whether existing datasets can be extended to include situational information.

heading: 2.2 Necessity of Situational Information
text: Most importantly, the absence of situational information leads to the underspecification of the problem space. Without knowing the situation in which an utterance is expressed, its interpretation cannot always be determined. For instance, the request “please call Pat” could mean at least two actions: speaking to Pat in person or making a phone call.
Additionally, without sufficient knowledge of the world state, systems may produce meaningless or contradictive responses even if they appear natural. In the research community, the inconsistency within generated responses is recognized to be one of the unsolved problems (Nie et al., 2021; Shuster et al., 2022). This problem may be attributed to the underspecified task setting. As previous examples suggest, the interpretation of human communication often relies on unspoken information. When situational information is absent, systems must assume implicit parameters of the world state on their own, which may not always be correct. For instance, the inconsistency of personality information had been a common challenge for chat bots (Li et al., 2016) and was alleviated by explicitly modeling user-based features (Zhang et al., 2018). Furthermore, training on this problem formulation may force systems to learn superficial patterns.
The challenge of evaluating conversation systems is also compounded by the broadness of the problem space. Previous studies have discredited the use of automatic evaluation methods in response generation tasks (Liu et al., 2016). Although techniques such as considering multiple
reference responses may alleviate this problem to some extent (Sai et al., 2020), it remains a significant challenge. Furthermore, even in the task of response selection, reliably evaluating system output is non-trivial due to the potential for false negatives when confusing distractor statements are included in the pool of candidate responses (Hedayatnia et al., 2022).

heading: 3 Situated Response Generation
text: In order to analyze the impact of incorporating situational information into response generation, we conducted an empirical analysis using two neural generation models and three English datasets.5

heading: 3.1 Datasets
text: We used the following English datasets.
1. SUGAR (Otani et al., 2023): This dataset consists of single-turn conversations in different help-seeking scenarios. Each example includes 12 sentences that describe situational information across six categories, including date, time, location, speaker’s behavior, environment, and speaker’s possession. Some of the statements are irrelevant and serve as distractors. SUGAR represents datasets that provide rich situational information.
2. CICERO (Ghosal et al., 2022): This dataset is a compilation of three datasets, including DailyDialog (Li et al., 2017), MuTual (Cui et al., 2020), and DREAM (Sun et al., 2019). CICERO is an example of conversational datasets that do not explicitly present situational information.6
3. ConvAI2 (Zhang et al., 2018; Dinan et al., 2020b): This dataset is designed for persona
5The purpose of this analysis is to find out if there are any notable patterns associated with the inclusion of situational statements rather than benchmarking response generation systems.
6Although CICERO includes annotations of commonsense reasoning about target utterances, we did not use them as they include unobservable facts. We only used CICERO for the pre-filtering it underwent.
chats, with each conversation featuring the speaker’s persona information in 4-5 sentences.7 ConvAI2 is a dataset with user-based features.
We selected 25 test instances for manual evaluation from the test split of each dataset. For CICERO and ConvAI2, which consist of multi-turn conversations, we randomly selected one target turn from each dialogue, and considered its preceeding utterances as conversation history. We chose targets of test instances the second to the fourth turn to reduce the cognitive load during evaluation. As the test split for ConvAI2 is not publicly available, we used its validation split as our test data and selected 1,000 examples for validation from the training split. Table 2 shows the dataset sizes after our filtering process.

heading: 3.2 Generating Situational Statements
text: CICERO and ConvAI2 do not contain descriptions of situational information. We utilized a Transformer-based generation model to automatically generate situational statements for these datasets, which allowed us to analyze how systems could generate situated responses within a specific context (See Appendix A.1 for details). Table 3 shows an example of generated situational statements.
To generate the situational information descriptions, we used the SUGAR dataset to fine-tune COMETDISTIL (West et al., 2022), which is a GPT2-XL model (Radford et al., 2019) trained on common-sense knowledge data. We concatenated a previous utterance, a response, and a reference
7We used revised persona statements.
situational statement into one sequence and trained the model to minimize a cross-entropy loss over the situation part. We also fine-tuned another COMETDISTIL (West et al., 2022) model without reference responses in input to avoid including the gold-standard information in testing instances. In input sequences, each text was headed by special symbols indicating the text type: <utterance> for an utterance, <response> for a response, and <situation category> for a situational statement. The <situation category> symbol is one of date, time, location, behavior, environment, and possession.
Using the fine-tuned model, we added 10 situational statements to each example, including one each for date, time, location, and behavior, and three each for environment and possession. Finally, for quality control, one of the authors manually checked the test samples from CICERO and ConvAI2 (25 for each) and corrected context statements when required (e.g., conflicting facts). The reference responses were hidden during the manual verification to avoid bias. This manual verification process ensures the quality of the test dataset in order to minimize the confusion of annotators in the following manual evaluation of responses.

heading: 3.3 Setup
text: Systems: Considering the reported performance and the availability of implementations, we chose the following baseline systems:
1. BlenderBot2 (BB2): A Transformer-based response generation model that is pre-trained on multiple conversational datasets. We used a distilled 400M-parameters model in the ParlAI library (Miller et al., 2017).
2. GPT-3: A Transformer-based causal language model that is pre-trained on a massive collection of documents. We used GPT-3-DaVinci (175B parameters) through OpenAI API. For each dataset, we manually selected four highquality training examples and embeded them in a prompt.
We fine-tuned BB2 on the mixture of the aforementioned datasets in a multi-task learning setting. We up-sampled SUGAR and CICERO to balance the data sizes. To alleviate the randomness of system output, we trained two BB2 models with different random seeds, and for each model, we generated one response by beam search with width 2. We obtained top-2 generations from GPT-3 with a beam
width of 4. Appendix A.2 describes implementation details.
Evaluation: We recruited three annotators on Amazon Mechanical Turk to evaluate each response.8 We employed three criteria: (1) grammaticality (whether the response is grammatically correct), (2) Coherence (whether the response is coherent and contextually appropriate), and (3) contextspecificality (whether the response is specifically relevant to the given context.) The latter two criteria were defined based on prior work (Thoppilan et al., 2022; Zhou et al., 2022).9 Table 4 shows some examples. We collected a total of 1,800 binary judgments for each criterion in our evaluation. The inter-annotator agreement was relatively low, with a Fleiss’ kappa of 0.38, likely due to the subjective nature of the quality assessment. The agreement for evaluating BB2 was notably low, possibly because its responses are often not fluent, making it difficult for annotators to rate individual criteria. In contrast, GPT-3’s responses were fluent, resulting in a Fleiss’ kappa of 0.48. To minimize the impact of this variance, we calculated the final score of each response by averaging the ratings of three annotators and manually reviewed responses with high variance.

heading: 3.4 Results
text: Figure 1 shows the average scores of responses. GPT-3 was highly capable of generating fluent responses based on its internal assumptions about the situations. The annotators was likely inferred the assumed situations to interpret the GPT-3’s responses, resulting in higher ratings. In the example below, GPT-3 produced appropriate responses regardless of the presence of situational information.
Example 1 A: “Hi, Mike! how are you feeling now?” B: “How did you know I was here? is it Tom?” A: “I was talking with Bob yesterday and I learnt your right leg had been injured. How did it happen?”
GPT-3 (Without situational information in input) (a) “Yeah, I slipped on the stairs and I sprained my
8Prior work provides multiple empirical results showing that automatic evaluation metrics are not strongly correlated with human judgments (Liu et al., 2016; Deriu et al., 2022). Therefore, we solely rely on manual evaluation in this study. See Appendix B for details.
9We adopted the definitions of Zhou et al. with modified terminology.
ankle.” (b) “It was a sports accident. I was playing basketball and I sprained my ankle.”
GPT-3 (+Situation: “Person A and Person B are in the hospital.”, ..., “Person B injured his right leg when he was playing baseball.”) (c) “Yeah, I was playing baseball and I twisted my ankle.” (d) “Yeah, I had a surgery last night. It happened when I was playing baseball. I’m feeling much better now, thanks.”
On the other hand, BB2 struggled to identify generalizable patterns through fine-tuning when situational information was not provided. This was evident in SUGAR, where only a single utterance was provided as input to the system. The following example shows responses generated by BB2.
Example 2 A: “Could you help me take a bus?”
BB2 (Without situational information in input) (a) *“Sure. shall I book a taxi as well? I can also book a ride for you to and from work.” (b) “Sure. would you like me to bring your bags, too? they are heavy.”
BB2 (+Situation: “The speaker has a bus schedule.” ... “There is a backpack near the desk.”) (c) “Sure. shall I pull up your bus schedule? it will show you when the bus is coming.” (d) “Sure. would you like me to get your bag ready? it will go in your backpack.”
BB2 produced more coherent and contextspecific responses when situational information was explicitly provided. However, as the examples above indicate, BB2’s responses were not as fluent as GPT-3’s, resulting in lower overall performance.
We observed that in some examples, GPT-3 selected information that was not directly relevant to the immediate previous utterance and returned unnatural responses like the example below.
Example 3 A: “Could you help me stretch?” GPT-3 (Without situational information in input) “Absolutely. Let me know which muscles you want to focus on and I can show you some stretches.” GPT-3 (+Situation: “The speaker is wearing a jogging suit and running shoes.”, ..., “There is a beach ball at home.” ... “The nearest beach is open.”) *“Of course! Let’s go to the beach and play with the beach ball while we stretch.”
Furthermore, as shown in Example 1 and Table 5, semantic consistency within the top-k responses improved when more situational information is given, although this was not evaluated in our experiments. This behavior is fundamentally rooted in the design of the task. Having inconsistent and mutuallyexclusive top-k candidates is not a problem when systems need to ground them on nothing.

heading: 3.5 GPT-3’s Robustness to Distractors
text: GPT-3 demonstrated superior performance in response generation, but our observations suggest that it can be misled by irrelevant situational statements (distractors) and return suboptimal responses. To further investigate the model’s robustness to distractors, we evaluated GPT-3 on SUGAR with varying numbers of situational statements.
Experimental Setup: We controlled the number of distractors by extending the SUGAR dataset, whose examples have about six distractors. We created instances with 5, 10, and 15 distractors by sampling statements from the whole dataset
as necessary. We selected 30 examples at random for this experiment and manually fixed contradictions within situational statements. We generated two responses for each example using GPT-3 through OpenAI’s API, employing the same hyperparameter setting used in the previous experiment.
Evaluation: We recruited two annotators on Amazon Mechanical Turk to evaluate each response. Based on previous annotation results, we updated the annotation guidelines and qualification criteria10, resulting in substantial agreement (Fleiss’ kappa of 0.76). We calculated average scores for three dimensions: (1) grammaticality, (2) coherence, and (3) context-specificity.
Results: The evaluation scores of GPT-3 are presented in Figure 2. The grammaticality and contextspecificity of responses remained at the same level regardless of the number of distractors, which suggests that GPT-3 is highly capable of integrating provided information into fluent English expressions. On the other hand, the coherence of a response, which requires the model to discern relevant information, exhibits a different trend. The scores are high when no or five distractors were presented (0.86 and 0.90 for 0 and 5 distractors, respectively), but the coherence scores decreased when more distractors were added (0.77 and 0.81 for 10 and 15 distractors, respectively).11 This is exemplified by the following model outputs.
Example 4 A: “Please lock the door.”
No distractors (“The speaker is dressed in nice clothing.” ..., “The door is unlocked.”, ..., “The windows are open.”) “Sure, and do you want me to close the windows too?”
5 distractors (+ “The speaker has a printout titled, Interior design for beginners.”, “Person A’s doctor told him he should lose weight.”, ...) ? “Sure, and do you need any help with the interior design project?”12
10 distractors (+ “The speaker has laundry piled up in the room.”, “Person A has scissors in the house.”, ...) ? “Sure, and should I turn off the windows too?”
10See Appendix B. 11There was a minor improvement in performance when the number of distractors rises from 10 to 15. The model might adapt to avoid conflating excessive information when it recognized a majority of the presented situational statements as irrelevant in training examples.
12This response might be acceptable given that the speaker has a printout about interior design.
15 distractors (+ “Person A has a telescope in the house.”, “Person A owns an exercise bike.”, ...) *“Sure, and would you like me to help you with the laundry too?”
The coherence score was relatively low (0.86) when no distractors were presented. We conjecture the model may have learned to copy phrases to generate responses instead of reasoning over input texts. We found that the model’s responses tended to sound abrupt, as shown in the following example:
Example 5 Person A: “Can you assist me with brushing my teeth?”
No distractors (“The speaker has a dental cleaning scheduled.”, ..., “There is no more toothpaste in the house.”) *“Sure, I can help you get some toothpaste at the store. Would you like me to come with you?”

heading: 4 Discussion
text: Models’ internal assumptions: As discussed in Section 2, the lack of sufficient situational information often makes the interpretation of utterances ambiguous. In such a setting, systems need to learn to make various assumptions about the world state to produce naturally-sounding language, which can be regarded as a form of hallucination. Responses generated in this way can be useless in real applications, where the world state is predetermined. Our empirical analysis also indicates that the systems’ consistency can be improved with detailed situational information, which is also aligned with the initial motivation of background-based conversational tasks like persona chat. On the other hand, our results indicates that GPT-3 can generate accurate responses even without the provision of situational information. This observation suggests that
large-scale language models might have already captured information about typical world states and appropriate behavior through pre-training. Nevertheless, there is no guarantee that the model’s internal assumption will always align perfectly with the actual world state. Hence, there remains a necessity to provide the model with situational information in some form.
Resource acquisition: Simple collections of textual conversations can be easily obtained at scale from the web, but acquiring their situational information is more difficult. For example, although conversations on Twitter may be grounded in the weather, sport events, and news on a particular day, automatically extracting such alignments may be challenging. The connection between utterances and related information is often obscure, and manual intervention is likely required to obtain highquality annotations. As a potential remedy for this challenge, we attempted automatic generation of situational information in our case study. The quality of the generated result was fair, but we needed to manually revise the test instances. Recent studies have demonstrated promising results in inducing world knowledge from PLMs (West et al., 2022; Ghosal et al., 2022). The future advancement in this line of work may make it possible to annotate existing open-domain conversation datasets with situational information in a post-hoc manner.
Availability: Different platforms of conversational systems have access to different types of situational information. Smart speakers may be equipped with physical sensors to observe visual and audio information. On the other hand, virtual assistants and text-based chatbots may not have access to such information. However, it is likely that there are some available signals that human communicators and systems could refer to, such as approaching holidays and personal information obtained through previous conversations. Finch et al. (2019) demonstrated that mentioning recent events can improve user engagement in chit-chat. Furthermore, if conversation systems have access to the Internet, which is often the case, they can access diverse kinds of information through external APIs. Access to APIs can also facilitate conversational assistance with task-specific information in various domains (Liang et al., 2023).
Representation: Prior work has demonstrated that a substantial range of surrounding information
can be represented and integrated by textual representations (Zhou et al., 2018; Zhang et al., 2018; Rashkin et al., 2019; Kim et al., 2022; Otani et al., 2023), and our study has also shown that textual statements can inform response generation models of situational information. However, it is important to note that certain types of information might be more effectively represented using alternative formats, such as images, audio signals, numerical values, or logical expressions. Future work should explore and develop methods to better represent situational information and incorporate it into computational models.
Adequacy: When situations are taken into account, a different problem arises. Our findings indicate that it is not straightforward to identify relevant situational information and integrate it into a coherent response, even with just 10 situational statements. Additionally, there is a technical limitation on the length of input that a model can handle. situational information can typically be obtained from various sources, and often, an excessive amount of information is present. Humans can quickly focus on crucial information and discard the rest, otherwise, it would take forever to read, process, and reason over surrounding information. Researchers have identified the Frame Problem (McCarthy and Hayes, 1969) that describes the dilemma of a reasoning system in determining which aspects of a situation change and which remain constant after an action. To date, there has been no satisfactory solution to this questions, making the challenge of situated conversation an interesting open challenge.
Common ground: Knowledge about situations is closely related to common ground–the information shared by conversation participants. Without common ground, conversation participants would need to convey every parameter of their message, which is extremely inefficient. The importance of common ground is widely recognized, and decades of dialogue research have been devoted to developing systems that can effectively establish common ground with their interlocutors by inferring, presenting, requesting, accepting, and repairing individual beliefs about various information through conversations (Traum and Allen, 1994; Clark, 1996; Poesio and Rieses, 2010; inter alia). In this paper, we did not delve into the problem of common ground, but the consideration of situations, which is our main proposal, is the first step
towards computational modeling of grounding.

heading: 5 Related Work
text: Conversation history: There is a rich line of work on how to induce useful contextual information from conversation history, for example, by designing dedicated components for capturing contextual information (Tian et al., 2017; Sankar et al., 2019) and using external knowledge (Young et al., 2018; Wu et al., 2020; inter alia). While conversation history contains rich information, we need to also incorporate situational information, which is often unspoken, and to this end, we should think about how to design tasks and datasets.
Prompt design: Our analysis is closely related to work on in-context learning, or prompting, with PLMs. In particular, much attention has been paid to the effective provision of demonstrative examples (Zhao et al., 2021; Liu et al., 2022; Min et al., 2022). This paper discussed the problem from a different perspective, namely what clues should be included in prompts (situations) and how PLMs perform (misleading by distractors). Our observation regarding the latter is consistent with prior work that revealed the vulnerability to perturbations in input (Elazar et al., 2021; Pandia and Ettinger, 2021). Future work should explore ways to robustly identify relevant situational information to generate optimal responses.

heading: 6 Conclusion
text: Our main claim is that situational information, which may or may not be stated explicitly by humans, should be represented and incorporated as input in open-domain conversational tasks and datasets in order to advance the capabilities of conversation systems. We posited that the absence of situational information results in an underspecified problem space, causing a severe problem for both the development and evaluation of conversation systems. Our experiments on three textual datasets highlight the benefits and difficulties of providing explicit and implicit situational information to response generation systems, which motivates future research on situated conversation systems.
Limitations
Firstly, we did not address the fundamental challenge of determining an adequate amount of situational information. It is very difficult, if not
impossible, to describe all the situations required to perform rationale reasoning, so we need to give up somewhere, relying on the reasoning capability of NLP systems.
Secondly, we did not use large-scale data or conduct an extensive search for optimal hyperparameters and prompts (for GPT-3) in our experiments as the primary goal of this study was to raise attentions to potential issues and benefits associated with situational informaiton. The models may have performed better with different configurations. We did not examined the capabilities of larger PLMs in conducting situated conversations at scale. In our empirical analysis, we opted for GPT-3 due to its transparency about technical details compared with later versions of GPT.
Finally, while situational information can aid in the development of truthful and creative response generation systems, it does not address well-known issues associated with conversational technologies, such as safety and bias. In fact, poorly chosen situational information may even amplify undesired bias by linking two irelevant concepts together. To mitigate this problem, researchers and developers should exercise caution when collecting data and carefully monitor system output.
Ethics Statements
The use of crowdsourcing: We recruited human evaluators in Amazon Mechanical Turk. Our evaluation task does not collect any personal information other than anonymized worker IDs and country of residence (due to our location-based worker qualification). We do not plan to release this information to the public. We set the task reward based on trial studies so that the estimated hourly rate would reach at least $9.00.
The risk in the inclusion of situational information: While we believe that incorporating situational information can have a positive impact on conversational technologies in general, as previously mentioned, it is not intended to address well-known issues concerning the toxic behavior of language generation models. Rather, it may introduce another source for models to learn undesirable associations between concepts and language. Therefore, the data and system output should be closely monitored, either manually or through automatic methods such as debiasing techniques (Liu et al., 2020; Dinan et al., 2020a).

heading: Acknowledgments
text: We thank Yonatan Bisk, Benjamin Van Durme, Lori Levin, and the anonymous reviewers for their feedback.

heading: B Crowdsourced Evaluation
text: B.1 First Experiment
In the first experiment we rectuited crowd workers on Amazon Mechanical Turk. We set the following qualification requirements for filtering workers: (1) at least 1,000 HITs are approved so far, (2) ≥ 99% approval rate, (iii) living in US. Each HIT involves judgment of three response candidates. Workes were paid $0.30 for each HIT. We used the guidelines and interface developed by (Zhou et al., 2022). Figure 3 shows the annotation guidelines. To monitor the performance of workers, we embedded one dummy response in each HIT. We created the dummy responses to be a clearly bad response.
Initially, we followed Zhou et al. (2022) and also evaluated if the responses are interesting or not, but we found the inter-annotator agreement of this criterion is high enough to draw a reliable conclusion (Fleiss’ kappa of 0.2). Therefore, we removed this criterion from our final results.
16https://chat.openai.com/
B.2 Second Experiment In the second experiment, we recruited workers who met the following qualifications: (1) The Mechanical Turk Masters Qualification has been granted by the platform, (2) Number of HITs approved ≥ 1,000, (3) HIT approval rate ≥ 95%, (4) Location is US. We increased a reward based on the numbder of distractors. ($0.35 for 10 distractors and $0.40 for 15 distractors.)

heading: Future Work
text: Our experiments on three textual datasets highlight the benefits and difficulties of providing explicit and implicit situational information to response generation systems, which motivates future research on situated conversation systems. Limitations
Firstly, we did not address the fundamental challenge of determining an adequate amount of situational information. It is very difficult, if not
impossible, to describe all the situations required to perform rationale reasoning, so we need to give up somewhere, relying on the reasoning capability of NLP systems. Secondly, we did not use large-scale data or conduct an extensive search for optimal hyperparameters and prompts (for GPT-3) in our experiments as the primary goal of this study was to raise attentions to potential issues and benefits associated with situational informaiton. The models may have performed better with different configurations. We did not examined the capabilities of larger PLMs in conducting situated conversations at scale. In our empirical analysis, we opted for GPT-3 due to its transparency about technical details compared with later versions of GPT. Finally, while situational information can aid in the development of truthful and creative response generation systems, it does not address well-known issues associated with conversational technologies, such as safety and bias. In fact, poorly chosen situational information may even amplify undesired bias by linking two irelevant concepts together. To mitigate this problem, researchers and developers should exercise caution when collecting data and carefully monitor system output