Do You Have any Recommendation? An Annotation System for the Seekers’ Strategies in Recommendation Dialogues
Authors: Martina Di Bratto, Riccardo Orrico, Ancuta Budeanu, Marta Maffia, Loredana Schettino (Università di Napoli Federico II, Università di Napoli L'Orientale, Università di Salerno, 2021)
Topic: Chatbots and Dialogue Systems

Abstract
The development of dialogue systems benefits from the study of the communication strategies used by human speakers. In the context of recommendation dialogue systems some researchers have investigated the sociable recommendation strategies employed by the Recommenders in natural settings to make successful and persuasive recommendations <cit.>. However, the Seeker’s contribution, as well as the Recommender’s, shapes the development of the communicative exchange, in that the Seekers may use specific strategies to disclose their preferences and reach their goal. So, modelling the Seeker’s communicative strategies along with the ones used by the Recommender may improve the efficiency of recommendation dialogue systems. In this work, we provide a reliable tagset for the Seekers utterances present in the Inspired dataset, defining a set of communicative strategies coherent with the already existing one for the Recommenders.

Introduction
Nowadays conversational recommendation systems seem to be acquiring a fundamental role in information seeking and retrieval. In a recent paper, Hayati and her colleagues <cit.> have argued for the need to study the communication strategies used by human speakers in a natural setting for developing dialogue systems that are able to make successful and persuasive recommendations. The authors have proposed Inspired, a dataset of recommendation dialogues collected in a realistic setting, enriched with a detailed annotation of the sociable recommendation strategies employed by the Recommender.  However, as in any interaction, these dialogues are the result of the cooperation between the interlocutors, who actively partake in both the construction of meaning and of the relationship among each other <cit.>: the Seeker’s contribution, as well as the Recommender’s, shapes the development of the communicative exchange, in that the seekers may use specific strategies to disclose their preferences and reach their goal, i.e., to get items that suit their needs. Hence, modelling the Seeker’s communicative strategies along with the ones used by the Recommender may improve the efficiency of recommendation dialogue systems.   In this work, we aim to fill this gap proposing a tagset for the Seekers communicative strategies that is coherent with the one previously provided for the Recommenders by Hayati and colleagues. The paper is structured as follows: recommendation dialogue systems are considered in relation to the Argumentation Theory ( <ref>) and the Inspired tagset <cit.> is described ( <ref>), then the tagset for the Seeker's strategies is presented ( <ref>), along with the data proving the reliability of the annotation scheme ( <ref>) and a preliminary analysis of the interactions ( <ref>).

Conclusion
This work is supported by the idea that studying communication strategies used by human speakers is fundamental to improve the performances of dialogue systems. This was already supported by Hayati and her colleagues (2020) who analyzed the Recommenders' sociable strategies in recommendation dialogues to develop successful and persuasive recommendation dialogue systems. However, considering the cooperative nature of dialogues, we argue that annotating the Seeker’s move may be pivotal in the training phase of recommendation dialogue systems. Hence, we propose an annotation scheme for the Seeker’s utterances that is coherent with the annotation of Recommender’s utterances. Considering the Seeker's role and main moves, we have drawn four categories: recommendation requests, global requesting strategies, giving preference strategies and local requesting strategies.Results on the reliability of the annotation scheme show that the agreement between the 5 annotators ranges from substantial to almost perfect <cit.> for most strategies but one, i.e., the strategy used to ask for movies of a specific genre. Similarly, observing the other cases of disagreement, we find that they mostly concern the identification of global and local requesting strategies. We showed that in most of these cases annotators failed to agree on whether an utterance contained a second strategy (manly a specific title request). In this cases, some annotators assigned a second label believing that the more specific request was generated as a conversational implicature stemming from the Seeker's mention of a certain movie title or attribute and the expression of his/her own opinions and experiences. The fact that most of the cases of disagreement fall within this situation might also explain why we registered high levels of disagreement for the get_from_genre label. Observing the confusion matrix (Figure 1), what can be noticed is that this category has been frequently confused with the no_label one. An explanation of this phenomenon could be found in utterances like "I love sci-fi movie" to which only the first label as personal_opinion_pos has been assigned. Nonetheless, other annotators also added get_from_genre as second label, for the reason explained above. We believe that this does not specifically depend on the strategy per se, but simply on the fact that genre is the feature of a movie that most frequently was mentioned by the Seekers (30% of the total features, as opposed to i.e. actors and directors, occurring respectively, in 20% and 4% of the cases), therefore more frequently led the annotators to assign different strategies. A finer analysis of the turn by turn strategies of the two participants on a larger number of dialogues would be informative about the extent to which Recomemenders make the inference (and act on it). This would help understand how to treat these cases. Concerning the general distribution of the Seekers’ strategies, positive personal opinion and non-present personal experience seem to be more frequent than the global and local requesting strategies. The strategies distribution along with the dialogue turns, on the other hand, shows that the first turns are mainly characterized by the occurrence of recommendation requests, reflecting the Recommender's strategy of offering help. In the middle of the conversation, requests for getting information or movie titles increase together with personal opinion and personal experience, even if the latter seems to be more equally distributed. This distribution could reflect the fundamental role of the Seeker in modelling the conversation. In the first phase of exploration the Seekers’ personal opinions are explicitly elicited by the Recommenders’ inquiries. Instead, in the exploitation phase, the Seeker could also provide soft evidence of their preferences, which may be used by the Recommender to help the Seeker find a suitable item. This attitude is very common in human-human dialogue with respect to the human-machine interaction, since it follows the principles of cooperative dialogue <cit.>.For this reason, Recommender systems that adopt a proactive behaviour and take the initiative to provide a piece of information that is not explicitly requested, should be able to better achieve the user needs and fulfil the goal of the dialogue <cit.>.