In information retrieval, facet identification of a user query is an important task. If a search service can recognize the facets of a user’s query, it has the potential to offer users a much broader range of search results. Previous studies can enhance facet prediction by leveraging retrieved documents and related queries obtained through a search engine. However, there are challenges in extending it to other applications when a search engine operates as part of the model. First, search engines are constantly updated. Therefore, additional information may change during training and test, which may reduce performance. The second challenge is that public search engines cannot search for internal documents. Therefore, a separate search system needs to be built to incorporate documents from private domains within the company. We propose two strategies that focus on a framework that can predict facets by taking only queries as input without a search engine. The first strategy is multi-task learning to predict SERP. By leveraging SERP as a target instead of a source, the proposed model deeply understands queries without relying on external modules. The second strategy is to enhance the facets by combining Large Language Model (LLM) and the small model. Overall performance improves when small model and LLM are combined rather than facet generation individually.

The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed.1) In general, collecting persona-grounded corpus is very expensive.2) The chatbot system does not always respond in consideration of persona at real applications.To address these challenges, we propose a plugand-play persona prompting method.Our system can function as a standard open-domain chatbot if persona information is not available.We demonstrate that this approach performs well in the zero-shot setting, which reduces the dependence on persona-ground training data.This makes it easier to expand the system to other languages without the need to build a persona-grounded corpus.Additionally, our model can be fine-tuned for even better performance.In our experiments, the zero-shot model improved the standard model by 7.71 and 1.04 points in the original persona and revised persona, respectively.The fine-tuned model improved the previous state-of-the-art system by 1.95 and 3.39 points in the original persona and revised persona, respectively.To the best of our knowledge, this is the first attempt to solve the problem of personalized response selection using prompt sequences.Our code is available on github 1 .

Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation.However, each grounding has been mostly researched in isolation with more practical multi-context dialogue tasks introduced in recent works.We define Persona and Knowledge Dual Context Identification as the task to identify persona and knowledge jointly for a given dialogue, which could be of elevated importance in complex multicontext dialogue settings.We develop a novel grounding retrieval method that utilizes all contexts of dialogue simultaneously.Our method requires less computational power via utilizing neural QA retrieval models.We further introduce our novel null-positive rank test which measures ranking performance on semantically dissimilar samples (i.e.hard negatives) in relation to data augmentation.

In emotion recognition in conversation (ERC), the emotion of the current utterance is predicted by considering the previous context, which can be utilized in many natural language processing tasks.Although multiple emotions can coexist in a given sentence, most previous approaches take the perspective of a classification task to predict only a given label.However, it is expensive and difficult to label the emotion of a sentence with confidence or multi-label.In this paper, we automatically construct a grayscale label considering the correlation between emotions and use it for learning.That is, instead of using a given label as a one-hot encoding, we construct a grayscale label by measuring scores for different emotions.We introduce several methods for constructing grayscale labels and confirm that each method improves the emotion recognition performance.Our method is simple, effective, and universally applicable to previous systems.The experiments show a significant improvement in the performance of baselines.

As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important.If the machine-generated sentences reflect emotion, more human-like sympathetic conversations are possible.Since emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, many studies reflect the dialogue context to improve the performances.Many recent approaches show performance improvement by combining knowledge into modules learned from external structured data.However, structured data is difficult to access in non-English languages, making it difficult to extend to other languages.Therefore, we extract the pre-trained memory using the pre-trained language model as an extractor of external knowledge.We introduce CoMPM, which combines the speaker's pre-trained memory with the context model, and find that the pre-trained memory significantly improves the performance of the context model.CoMPM achieves the first or second performance on all data and is state-of-the-art among systems that do not leverage structured data.In addition, our method shows that it can be extended to other languages because structured knowledge is not required, unlike previous methods.Our code is available on github 1 .

Text style transfer is the task that generates a sentence by preserving the content of the input sentence and transferring the style.Most existing studies are progressing on non-parallel datasets because parallel datasets are limited and hard to construct.In this work, we introduce a method that follows two stages in nonparallel datasets.The first stage is to delete attribute markers of a sentence directly through a classifier.The second stage is to generate a transferred sentence by combining the content tokens and the target style.We experiment on two benchmark datasets and evaluate context, style, fluency, and semantic.It is difficult to select the best system using only these automatic metrics, but it is possible to select stable systems.We consider only robust systems in all automatic evaluation metrics to be the minimum conditions that can be used in real applications.Many previous systems are difficult to use in certain situations because performance is significantly lower in several evaluation metrics.However, our system is stable in all automatic evaluation metrics and has results comparable to other models.Also, we compare the performance results of our system and the unstable system through human evaluation.Our code and data are available at the link 1 .

We study mechanisms for environments in which only some of the agents are directly connected to a mechanism designer and the other agents can participate in a mechanism only through the connected agents' referrals. Agents' network connections are private information, which can be strategically concealed by avoiding making referrals; while the mechanism designer wants to elicit the agents' private information about both payoff types and network connections. The set of feasible allocations is endogenously determined by the actual participants induced by referrals.

Systematic performance appraisal and ranking of candidates applying for promotion is important in strategic human resource management. In military organizations, it is particularly crucial to evaluate candidates for promotion and placement because their functions directly impact operations and national security. This paper proposes a framework that can be used to evaluate candidates based on unique performance evaluation criteria. The framework allows determination of the most qualified candidate by considering both quantitative scores and qualitative characteristics of his or her performance. It also ensures fairness, objectivity and transparency since evaluators first determine the metrics of performance evaluation as well as the weighting among the metrics before aggregating the appraisal scores to determine the ranking of each candidate. The ranking is determined by applying the fuzzy set operations and membership function. In order to decide the promotion rank of candidates, we propose a fuzzy ranking procedure in conjunction with a novel integrated performance appraisal and promotion ranking system.