3,324 research outputs found
SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding
In the evolving landscape of artificial intelligence, multimodal and
Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on
the identification and interaction with entities and their relations across
diverse modalities. Addressing the need for complex querying and interaction in
this context, we introduce SNeL (Structured Neuro-symbolic Language), a
versatile query language designed to facilitate nuanced interactions with
neural networks processing multimodal data. SNeL's expressive interface enables
the construction of intricate queries, supporting logical and arithmetic
operators, comparators, nesting, and more. This allows users to target specific
entities, specify their properties, and limit results, thereby efficiently
extracting information from a scene. By aligning high-level symbolic reasoning
with low-level neural processing, SNeL effectively bridges the Neuro-Symbolic
divide. The language's versatility extends to a variety of data types,
including images, audio, and text, making it a powerful tool for multimodal
scene understanding. Our evaluations demonstrate SNeL's potential to reshape
the way we interact with complex neural networks, underscoring its efficacy in
driving targeted information extraction and facilitating a deeper understanding
of the rich semantics encapsulated in multimodal AI models
Goal-oriented Dialogue Policy Learning from Failures
Reinforcement learning methods have been used for learning dialogue policies.
However, learning an effective dialogue policy frequently requires
prohibitively many conversations. This is partly because of the sparse rewards
in dialogues, and the very few successful dialogues in early learning phase.
Hindsight experience replay (HER) enables learning from failures, but the
vanilla HER is inapplicable to dialogue learning due to the implicit goals. In
this work, we develop two complex HER methods providing different trade-offs
between complexity and performance, and, for the first time, enabled HER-based
dialogue policy learning. Experiments using a realistic user simulator show
that our HER methods perform better than existing experience replay methods (as
applied to deep Q-networks) in learning rate
Improving Topic Segmentation by Injecting Discourse Dependencies
Recent neural supervised topic segmentation models achieve distinguished
superior effectiveness over unsupervised methods, with the availability of
large-scale training corpora sampled from Wikipedia. These models may, however,
suffer from limited robustness and transferability caused by exploiting simple
linguistic cues for prediction, but overlooking more important inter-sentential
topical consistency. To address this issue, we present a discourse-aware neural
topic segmentation model with the injection of above-sentence discourse
dependency structures to encourage the model make topic boundary prediction
based more on the topical consistency between sentences. Our empirical study on
English evaluation datasets shows that injecting above-sentence discourse
structures to a neural topic segmenter with our proposed strategy can
substantially improve its performances on intra-domain and out-of-domain data,
with little increase of model's complexity.Comment: Accepted to the 3rd Workshop on Computational Approaches to Discourse
(CODI-2022) at COLING 202
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