Semantic Representation and Inference for NLP
- URL: http://arxiv.org/abs/2106.08117v1
- Date: Tue, 15 Jun 2021 13:22:48 GMT
- Title: Semantic Representation and Inference for NLP
- Authors: Dongsheng Wang
- Abstract summary: This thesis investigates the use of deep learning for novel semantic representation and inference.
We contribute the largest publicly available dataset of real-life factual claims for the purpose of automatic claim verification.
We operationalize the compositionality of a phrase contextually by enriching the phrase representation with external word embeddings and knowledge graphs.
- Score: 2.969705152497174
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Semantic representation and inference is essential for Natural Language
Processing (NLP). The state of the art for semantic representation and
inference is deep learning, and particularly Recurrent Neural Networks (RNNs),
Convolutional Neural Networks (CNNs), and transformer Self-Attention models.
This thesis investigates the use of deep learning for novel semantic
representation and inference, and makes contributions in the following three
areas: creating training data, improving semantic representations and extending
inference learning. In terms of creating training data, we contribute the
largest publicly available dataset of real-life factual claims for the purpose
of automatic claim verification (MultiFC), and we present a novel inference
model composed of multi-scale CNNs with different kernel sizes that learn from
external sources to infer fact checking labels. In terms of improving semantic
representations, we contribute a novel model that captures non-compositional
semantic indicators. By definition, the meaning of a non-compositional phrase
cannot be inferred from the individual meanings of its composing words (e.g.,
hot dog). Motivated by this, we operationalize the compositionality of a phrase
contextually by enriching the phrase representation with external word
embeddings and knowledge graphs. Finally, in terms of inference learning, we
propose a series of novel deep learning architectures that improve inference by
using syntactic dependencies, by ensembling role guided attention heads,
incorporating gating layers, and concatenating multiple heads in novel and
effective ways. This thesis consists of seven publications (five published and
two under review).
Related papers
- How well do distributed representations convey contextual lexical semantics: a Thesis Proposal [3.3585951129432323]
In this thesis, we examine the efficacy of distributed representations from modern neural networks in encoding lexical meaning.
We identify four sources of ambiguity based on the relatedness and similarity of meanings influenced by context.
We then aim to evaluate these sources by collecting or constructing multilingual datasets, leveraging various language models, and employing linguistic analysis tools.
arXiv Detail & Related papers (2024-06-02T14:08:51Z) - Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization [0.0]
This paper presents a novel framework for abstractive TS of single documents.
It integrates three dominant aspects: structure, semantic, and neural-based approaches.
Results indicate significant improvements in handling rare and OOV words.
arXiv Detail & Related papers (2024-04-08T18:33:59Z) - Enhancing Argument Structure Extraction with Efficient Leverage of
Contextual Information [79.06082391992545]
We propose an Efficient Context-aware model (ECASE) that fully exploits contextual information.
We introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information.
Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance.
arXiv Detail & Related papers (2023-10-08T08:47:10Z) - UniDiff: Advancing Vision-Language Models with Generative and
Discriminative Learning [86.91893533388628]
This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC)
UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
arXiv Detail & Related papers (2023-06-01T15:39:38Z) - Imitation Learning-based Implicit Semantic-aware Communication Networks:
Multi-layer Representation and Collaborative Reasoning [68.63380306259742]
Despite its promising potential, semantic communications and semantic-aware networking are still at their infancy.
We propose a novel reasoning-based implicit semantic-aware communication network architecture that allows multiple tiers of CDC and edge servers to collaborate.
We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users.
arXiv Detail & Related papers (2022-10-28T13:26:08Z) - Pretraining on Interactions for Learning Grounded Affordance
Representations [22.290431852705662]
We train a neural network to predict objects' trajectories in a simulated interaction.
We show that our network's latent representations differentiate between both observed and unobserved affordances.
Our results suggest a way in which modern deep learning approaches to grounded language learning can be integrated with traditional formal semantic notions of lexical representations.
arXiv Detail & Related papers (2022-07-05T19:19:53Z) - Dynamic Inference with Neural Interpreters [72.90231306252007]
We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules.
inputs to the model are routed through a sequence of functions in a way that is end-to-end learned.
We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
arXiv Detail & Related papers (2021-10-12T23:22:45Z) - Infusing Finetuning with Semantic Dependencies [62.37697048781823]
We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models.
We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
arXiv Detail & Related papers (2020-12-10T01:27:24Z) - Neuro-Symbolic Representations for Video Captioning: A Case for
Leveraging Inductive Biases for Vision and Language [148.0843278195794]
We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning.
Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
arXiv Detail & Related papers (2020-11-18T20:21:19Z) - Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards [13.753240692520098]
We present a neural network architecture for joint coreference resolution and semantic role labeling for English.
We use reinforcement learning to encourage global coherence over the document and between semantic annotations.
This leads to improvements on both tasks in multiple datasets from different domains.
arXiv Detail & Related papers (2020-10-12T09:36:24Z) - Distributional semantic modeling: a revised technique to train term/word
vector space models applying the ontology-related approach [36.248702416150124]
We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings)
Vec2graph is a Python library for visualizing word embeddings (term embeddings in our case) as dynamic and interactive graphs.
arXiv Detail & Related papers (2020-03-06T18:27:39Z)
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.