What A Situated Language-Using Agent Must be Able to Do: A Top-Down
Analysis
- URL: http://arxiv.org/abs/2302.08590v1
- Date: Thu, 16 Feb 2023 21:30:26 GMT
- Title: What A Situated Language-Using Agent Must be Able to Do: A Top-Down
Analysis
- Authors: David Schlangen
- Abstract summary: Even in our increasingly text-intensive times, the primary site of language use is situated, co-present interaction.
This paper attempts a top-down analysis of what the demands are that unrestricted situated interaction makes on the participating agent.
- Score: 16.726800816202033
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Even in our increasingly text-intensive times, the primary site of language
use is situated, co-present interaction. It is primary ontogenetically and
phylogenetically, and it is arguably also still primary in negotiating everyday
social situations. Situated interaction is also the final frontier of Natural
Language Processing, where, compared to the area of text processing, very
little progress has been made in the past decade, and where a myriad of
practical applications is waiting to be unlocked. While the usual approach in
the field is to reach, bottom-up, for the ever next "adjacent possible", in
this paper I attempt a top-down analysis of what the demands are that
unrestricted situated interaction makes on the participating agent, and suggest
ways in which this analysis can structure computational models and research on
them. Specifically, I discuss representational demands (the building up and
application of world model, language model, situation model, discourse model,
and agent model) and what I call anchoring processes (incremental processing,
incremental learning, conversational grounding, multimodal grounding) that bind
the agent to the here, now, and us.
Related papers
- Bidirectional Emergent Language in Situated Environments [4.950411915351642]
We introduce two novel cooperative environments: Multi-Agent Pong and Collectors.
optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one.
We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination.
arXiv Detail & Related papers (2024-08-26T21:25:44Z) - Common Ground Tracking in Multimodal Dialogue [13.763043173931024]
We present a method for automatically identifying the current set of shared beliefs and questions under discussion'' (QUDs) of a group with a shared goal.
We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration.
We cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations.
arXiv Detail & Related papers (2024-03-26T00:25:01Z) - Neural Conversation Models and How to Rein Them in: A Survey of Failures
and Fixes [17.489075240435348]
Recent conditional language models are able to continue any kind of text source in an often seemingly fluent way.
From a linguistic perspective, contributing to a conversation is high.
Recent approaches try to tame the underlying language models at various intervention points.
arXiv Detail & Related papers (2023-08-11T12:07:45Z) - Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time.
The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z) - DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations.
We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
arXiv Detail & Related papers (2023-06-15T10:41:23Z) - An Interleaving Semantics of the Timed Concurrent Language for
Argumentation to Model Debates and Dialogue Games [0.0]
We propose a language for modelling concurrent interaction between agents.
Such a language exploits a shared memory used by the agents to communicate and reason on the acceptability of their beliefs.
We show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents.
arXiv Detail & Related papers (2023-06-13T10:41:28Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z) - Stabilized In-Context Learning with Pre-trained Language Models for Few
Shot Dialogue State Tracking [57.92608483099916]
Large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks.
For more complex tasks such as dialogue state tracking (DST), designing prompts that reliably convey the desired intent is nontrivial.
We introduce a saliency model to limit dialogue text length, allowing us to include more exemplars per query.
arXiv Detail & Related papers (2023-02-12T15:05:10Z) - Towards Interactive Language Modeling [18.925337115380703]
Motivated by these considerations, we pioneer the space of interactive language modeling.
We present a road map in which we detail the steps that need to be taken towards interactive language modeling.
This work aims to be the start of a larger research agenda on interactive language modeling.
arXiv Detail & Related papers (2021-12-14T18:35:02Z) - Positioning yourself in the maze of Neural Text Generation: A
Task-Agnostic Survey [54.34370423151014]
This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc.
We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
arXiv Detail & Related papers (2020-10-14T17:54:42Z) - How Far are We from Effective Context Modeling? An Exploratory Study on
Semantic Parsing in Context [59.13515950353125]
We present a grammar-based decoding semantic parsing and adapt typical context modeling methods on top of it.
We evaluate 13 context modeling methods on two large cross-domain datasets, and our best model achieves state-of-the-art performances.
arXiv Detail & Related papers (2020-02-03T11:28:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.