Communication between human and mobile agents is getting increasingly important as such agents are widely deployed in our daily lives. Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent's ability to interact with humans for as
sistance and navigate based on natural language responses. In this paper, we explore the Navigation from Dialogue History (NDH) task, which is based on the Cooperative Vision-and-Dialogue Navigation (CVDN) dataset, and present a state-of-the-art model which is built upon Vision-Language transformers. However, despite achieving competitive performance, we find that the agent in the NDH task is not evaluated appropriately by the primary metric -- Goal Progress. By analyzing the performance mismatch between Goal Progress and other metrics (e.g., normalized Dynamic Time Warping) from our state-of-the-art model, we show that NDH's sub-path based task setup (i.e., navigating partial trajectory based on its correspondent subset of the full dialogue) does not provide the agent with enough supervision signal towards the goal region. Therefore, we propose a new task setup called NDH-Full which takes the full dialogue and the whole navigation path as one instance. We present a strong baseline model and show initial results on this new task. We further describe several approaches that we try, in order to improve the model performance (based on curriculum learning, pre-training, and data-augmentation), suggesting potential useful training methods on this new NDH-Full task.
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, t
he users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.
In this paper we argue that embodied multimodal agents, i.e., avatars, can play an important role in moving natural language processing toward deep understanding.'' Fully-featured interactive agents, model encounters between two people,'' but a langu
age-only agent has little environmental and situational awareness. Multimodal agents bring new opportunities for interpreting visuals, locational information, gestures, etc., which are more axes along which to communicate. We propose that multimodal agents, by facilitating an embodied form of human-computer interaction, provide additional structure that can be used to train models that move NLP systems closer to genuine understanding'' of grounded language, and we discuss ongoing studies using existing systems.
The objective of the research is to determine the level of customer satisfaction with the
performance of the members of the distribution channels in Joud Company for electrical
household appliances according to the following characteristics: honest
y, responsibility,
knowledge, skills, presentation and negotiation. Through conducting a field study through
which a segment of the customers who are targeting the distribution outlets of the company
were targeted. The sample of the study was a soft sample from the previous society. The
sample consisted of (200) individual, distributed to the customers of the company, and 176
(complete questionnaire) were retrieved and valid for the statistical analysis , With a
response rate of (88%). The questionnaire was designed from 31 words distributed on five
axes that included the characteristics of the members of the distribution channels. The
research reached a number of results, the most important of which is that the level of
customer satisfaction with the performance of the members of the distribution channels
regarding the level of honesty, responsibility, knowledge, skills, presentation and
negotiation in Joud Company for household and electrical appliances is high.
This research traces, after conducting a wide literature survey, the areas not covered by prominent agent oriented software engineering (AOSE) methodologies. Each methodology has its strength and weakness and focuses on some stages of software
devel
opment lifecycle but not all stages. This paper presents an addition to a well established AOSE methodology (MaSE). MaSE is considered one of the strongest in the field, it does not, however, support handling early requirements. This work integrates MaSE with another methodology known for its strength in early requirement representation. The integration implied the development of a wide set of translation rules between two different environments of notations and graphical representations. A software tool was developed to automate the translation and a case study is used to demonstrate the work.
