Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning

When a natural language generation (NLG) component is implemented in a real-world task-oriented dialogue system, it is necessary to generate not only natural utterances as learned on training data but also utterances adapted to the dialogue environment (e.g., noise from environmental sounds) and the user (e.g., users with low levels of understanding ability). Inspired by recent advances in reinforcement learning (RL) for language generation tasks, we propose ANTOR, a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning. In ANTOR, a natural language understanding (NLU) module, which corresponds to the user's understanding of system utterances, is incorporated into the objective function of RL. If the NLG's intentions are correctly conveyed to the NLU, which understands a system's utterances, the NLG is given a positive reward. We conducted experiments on the MultiWOZ dataset, and we confirmed that ANTOR could generate adaptive utterances against speech recognition errors and the different vocabulary levels of users.Comment: Accepted by COLING 202

Efficacy and Safety of Ramucirumab/nab-paclitaxel for Previously Treated Advanced Gastric Cancer in Community Hospitals

As the nanoparticle albumin-bound paclitaxel (nab-PTX) is free of ethanol and premedication, the duration of administration is shorter and patients can drive themselves to and from the hospital. In the 2018 Japanese gastric cancer treatment guidelines, ramucirumab (RAM) plus weekly nab-PTX is conditionally recommended for previously treated patients with advanced gastric cancer. Here, we retrospectively analysed the efficacy and safety of RAM+nab-PTX for such patients in community hospitals. From January 2018 to December 2019, 43 patients with metastatic and recurrent gastric cancer received RAM+nab-PTX treatment. Six patients (13.9%) were older than 80 years and 9 patients (20.9%) showed ECOG-PS 2. Progression-free survival (PFS), overall survival (OS), overall response rate (ORR), disease control rate (DCR), and adverse events (AEs) were reviewed retrospectively. Median PFS was 114 days (95% confidence interval [CI]: 84-190) and median OS was 297 days (95% CI: 180-398). ORR and DCR were 32.4% and 72.2%, respectively. The incidence rates of ≥grade 3 neutropenia and febrile neutropenia were 53.5% and 2.3%, respectively. No treatment-related deaths occurred. RAM plus nab-PTX combination therapy demonstrated manageable toxicity even patients who were elderly or had an ECOG-PS 2. This treatment is useful in community hospital settings

Comparison of cellular characteristics between human hepatoma cell lines with wild-type p53 and those with mutant-type p53 gene

Characteristics of human hepatoma cell lines with the wild-type p53 were compared with those of human hepatoma cell lines with the mutant-type p53. The p21 protein located downstream of p53 was expressed in cell lines with the wild-type p53 but was not expressed in cell lines with the mutant-type p53. As to other tumor suppressor genes such as p16 and p27, there was no difference in their expression between both types of cell lines. In addition, no marked difference was observed in the activities of CDK2 and CDK4 between cell lines with the wild-type and the mutant-type p53. Phosphorylated Rb protein was detected in all cell lines except the HLE line, indicating that this cell line may have a deletion of and/or a mutation of the Rb gene. These results indicate that abnormalities of tumor suppressor genes other than p53, p16, p27, and Rb may be involved in hepatocarcinogenesis. The population doubling time of the wild-type p53 cells was significantly longer than that of the mutant p53 cells. Neither type of cell line showed a specific chromosome distribution which would indicate karyotype instability. The cell lines expressing the wild-type p53 produced tumors at lower frequency than those with the mutant p53 gene. Although there was no significant difference in effects of TGF-&#946;1, EGF, cholera toxin, and db-cAMP on cell growth between the two types of cells, all three cell lines with the wild-type p53 were resistant to cytotoxicity of TNF-&#945;, while two of the three with the mutant p53 were very sensitive to its cytotoxic effects.</p

Personality prediction from task-oriented and open-domain human–machine dialogues

Abstract If a dialogue system can predict the personality of a user from dialogue, it will enable the system to adapt to the user’s personality, leading to better task success and user satisfaction. In a recent study, personality prediction was performed using the Myers–Briggs Type Indicator (MBTI) personality traits with a task-oriented human–machine dialogue using an end-to-end (neural-based) system. However, it is still not clear whether such prediction is generally possible for other types of systems and user personality traits. To clarify this, we recruited 378 participants, asked them to fill out four personality questionnaires covering 25 personality traits, and had them perform three rounds of human–machine dialogue with a pipeline task-oriented dialogue system or an end-to-end task-oriented dialogue system. We also had another 186 participants do the same with an open-domain dialogue system. We then constructed BERT-based models to predict the personality traits of the participants from the dialogues. The results showed that prediction accuracy was generally better with open-domain dialogue than with task-oriented dialogue, although Extraversion (one of the Big Five personality traits) could be predicted equally well for both open-domain dialogue and pipeline task-oriented dialogue. We also examined the effect of utilizing different types of dialogue on personality prediction by conducting a cross-comparison of the models trained from the task-oriented and open-domain dialogues. As a result, we clarified that the open-domain dialogue cannot be used to predict personality traits from task-oriented dialogue, and vice versa. We further analyzed the effects of system utterances, task performance, and the round of dialogue with regard to the prediction accuracy

Clarifying the Dialogue-Level Performance of GPT-3.5 and GPT-4 in Task-Oriented and Non-Task-Oriented Dialogue Systems

Although large language models such as ChatGPT and GPT-4 have achieved superb performances in various natural language processing tasks, their dialogue performance is sometimes not very clear because the evaluation is often done on the utterance level where the quality of an utterance given context is the evaluation target. Our objective in this work is to conduct human evaluations of GPT-3.5 and GPT-4 to perform MultiWOZ and persona-based chat tasks in order to verify their dialogue-level performance in task-oriented and non-task-oriented dialogue systems. Our findings show that GPT-4 performs comparably with a carefully created rule-based system and has a significantly superior performance to other systems, including those based on GPT-3.5, in persona-based chat