What are the current limitations of natural language processing in human-robot interaction?
Best insight from top research papers
Crafting good human models for human-robot interaction (HRI) is challenging due to the need for significant prior knowledge and large amounts of interaction data. However, large-language models (LLMs) show promise as zero-shot human models for HRI. LLMs, which have consumed vast amounts of human-generated text data, can achieve performance comparable to purpose-built models in social datasets . Despite their potential, LLMs have limitations such as sensitivity to prompts and spatial/numerical reasoning mishaps . Another aspect of language processing in HRI is the need to improve spoken language interaction capabilities. Our ability to provide effective spoken language interaction with robots is still limited, and key scientific and engineering advances are needed in areas such as modeling social and interactive aspects of language, improving robustness, and integrating speech and language with other communication modalities .
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
38 Citations | The paper does not explicitly mention the current limitations of natural language processing in human-robot interaction. |
01 Jan 2022 16 Citations | The paper does not explicitly mention the current limitations of natural language processing in human-robot interaction. |
06 Mar 2023 | The paper does not mention the current limitations of natural language processing in human-robot interaction. |
Open access•Posted Content | The paper does not explicitly mention the current limitations of natural language processing in human-robot interaction. |
2 Citations | The paper does not explicitly mention the limitations of natural language processing in human-robot interaction. |
Related Questions
How do humans communicate with computers through natural language processing?5 answersHumans communicate with computers through natural language processing (NLP) by enabling machines to understand and interpret human language. NLP, a subset of artificial intelligence, facilitates computers in comprehending human language as it is written and spoken. This involves processes like sentence segmentation, tokenization, speech prediction, lemmatization, dependency parsing, named entity recognition, and coreference resolution. By storing associations between concepts and unique identifiers in databases, NLP systems break down text strings into sub-concepts, join them into higher-order concepts, and store them for communication with computers. Through NLP, machines can extract information from large text data, perform document classification, sentiment analysis, information extraction, language translation, and transcription of spoken words into written text.
Issues in human computer interaction4 answersHuman-Computer Interaction (HCI) encompasses various professional issues, challenges, and advancements in the field. These include ethical considerations, user-centered design practices, socio-cultural implications, legal aspects, and the impact of emerging technologies on HCI professionals. Geopolitical issues in HCI involve reconciling diverse discourses such as military power and emancipatory ambitions, as well as understanding the cultural and sociotechnical dynamics that shape the field. HCI has evolved to incorporate multidisciplinary approaches, theories, and methods from disciplines like anthropology and sociology. It addresses a wide range of topics, including techno-spirituality, global crises, disabilities, and emerging technologies like wearable devices and virtual reality applications. HCI has undergone three waves of development, with the third wave emphasizing emotional aspects and holistic user experiences. In healthcare, HCI aligns with trends like patient-centered care and shared decision making, but challenges remain unresolved. HCI focuses on designing user-friendly interfaces and improving interface designs to meet user expectations and enhance daily life experiences.
What is the software engineering problem that natural language processing techniques address?4 answersNatural language processing (NLP) techniques address the software engineering problem of improving the quality of software requirements by reducing ambiguity and extracting relevant information from natural language software requirements. These techniques can be used to automatically extract software system requirement specifications, cluster relevant requirement sentences, and identify actors and actions in the requirements. NLP can also be applied to detect violent content in text documents, such as insults and abusive comments, which is a problem arising from the quick development of communication through new technology media. Additionally, NLP techniques can be used to address the "total recall problem" in software engineering, which involves supporting large literature reviews, identifying software security vulnerabilities, test case prioritization, and static warning identification.
What are the strengths and gaps of natural language processing?5 answersNatural language processing (NLP) has strengths and gaps. The strengths of NLP include its ability to automatically process and analyze large amounts of textual data. It has diverse applications, such as enhancing student achievement, satisfaction, and accessibility through mobile note-taking apps. NLP can also be used to recreate surgical status from clinical narratives, although missing or inaccurately reported information can lead to misclassification. On the other hand, NLP faces challenges in its integration into sociology, as it was institutionalized outside of the field. Validity of digital data and different viewpoints associated with predictive and causal approaches are also concerns. In the field of plagiarism detection, NLP techniques have been used, but there are gaps in terms of methodologies and the need for further research. Overall, NLP shows promise but requires ongoing development and evaluation.
What are the strengths and gaps of natural language processing in chatbot?5 answersNatural language processing (NLP) in chatbots has strengths and gaps. ChatGPT, a large language model, has impressive language generation skills and has been widely adopted. It can process and generate texts in multiple languages due to its multilingual training data. However, there are limitations to ChatGPT, such as biased responses and perpetuation of harmful language patterns. Additionally, ChatGPT's performance for different NLP tasks and languages is worse compared to previous models, calling for further research and development. In the context of education, ChatGPT's performance is near-perfect for introductory courses but not advanced courses, and it can be fooled by minor modifications to text. These findings highlight the need for ethical considerations and improvements in NLP models for chatbots.
What are the factors that influence the use of natural language processing?4 answersFactors that influence the use of natural language processing include structural information, motivation, age, personality, self-confidence, experience or multilingualism, learning styles, aptitude, native language, teacher and course book, time-management, authentic materials, and native speaker. Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics that focuses on the interactions between computers and human languages or natural languages. In the context of medical education, the quality of feedback received by residents can be examined using natural language processing (NLP). Attending surgeons have a significant impact on the quality of feedback received by trainees.