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Research Proposal Topic on Attention Mechanism for Natural Language Processing with Deep Learning

Research Proposal Topic on Attention Mechanism for Natural Language Processing with Deep Learning

   The attention mechanism is increasingly popular in various neural architectures for Natural Language Processing (NLP). Currently, attention mechanisms have attracted broad interest in the NLP community owing to their capability to perform parallelizable computation, need significantly less training time, and flexibility in modeling context dependencies.
   Attention mechanisms are effectively employed for different input parts, different representations of the same data, or different features, to attain a compact representation of the data and highlight the relevant information dynamically. Intermingling attention in neural architectures may thus produce an outstanding performance gain, and the attention mechanism is also used as a tool for examining the behavior of the network.
   Attention mechanism attained significant advances in various tasks in natural language processing, including sentiment classification, text summarization, question answering, dependency parsing, multiple interacting sequence handling, and many more.

Categories of Attention Mechanism:
   Different attention mechanism variants have emerged for more complex NLP tasks. Most representative categories of attention mechanisms based on the input and output representation, namely, multi-dimensional attention, hierarchical attention, self-attention, memory-based attention, and task-specific attention, are listed below.
Multi-dimensional Attention Mechanism: Entraps multiple interactions between different representation spaces, which can be effortlessly built by directly stacking together multiple single-dimensional representations. Short-text intention recognition, dialogue response generation, and question answering are the recent application of multi-dimensional attention mechanisms in NLP.

Hierarchical Attention Mechanism: Effectively identifies and extracts important information globally and locally, either bottom-up (word-level to sentence-level) or top-down (word-level to character-level). A few of the hierarchical attention mechanism applied tasks in NLP are document classification, text generation, and text summarization.

Self-attention Mechanism: Learn deep and complex contextual representation self-adaptive and produce sustainable enhancement in different NLP tasks by focusing on a subset of input words. A selective self-attention network is a novel improvement that outperforms the self-attention mechanism in semantic role labeling and machine translation tasks.

Memory-based Attention Mechanism: Efficiently determine latent dependencies in enlightened NLP tasks and these mechanism is more powerful due to enabling reusability and increased flexibility with the incorporation of additional functionalities. Currently, multi-turn response retrieval is implemented using a memory-based attention mechanism.

Task-specific Attention Mechanism: It is significantly designed to capture essential information specified by the task and fit properly to their task. Machine translation, abstractive document summarization, and text classification are some of the notable applications of task-specific attention mechanisms.

Application Framework of Attention Mechanism: The attention mechanism, along with its variants, has been widely applied to several NLP tasks. Some application frameworks to explore the connection of attention to other abstract concepts in deep learning include ensemble, gating, and pre-training.
Attention for Ensemble: More representative power and flexibility is attained by an ensemble set of word embeddings in the attention model to build meta-embedding depending on the characteristics of the word.

Attention for Gating: The attention mechanism is integrated with memory updates in the recurrent network that permit context-aware updates and easier interpretability.

Attention for pre-training: pre-training methods are combined attention-based techniques with deep neural architectures, aspire to learn a higher quality token representation that includes syntactic and semantic information from the surrounding contexts, and the model is also fine-tuned to adapt to the downstream supervised task. Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Training (GPT) are popular attention-based pre-trained models for NLP tasks.

Key Application Tasks in NLP:
In neural machine translation, the alignment of sentences in different languages is a crucial problem while translating text from one language to another, especially for longer sentences. Henceforth, attention architecture enhances the alignment of sentences and boosts translation performance.

Text Classification has broad applications such as topic labeling, sentiment classification, and spam detection. These classification tasks use self-attention to build more effective document representation.

In addition, text matching is also the main research problem in NLP and information retrieval, which comprises question answering, document search, entailment classification, paraphrase identification, and recommendation with reviews. More novel research is recently evolving in text-matching and information retrieval in conjunction with attention comprehension.

Research Challenges and Future Directions:
Attention mechanism has gained interest from researchers as it owns the prospect of not lower the performance of neural networks for longer sequences, empowering as a tool for the integration of symbolic representations within neural architectures and successfully applied for a wide range of NLP tasks.
Attention for Deep Networks Investigation: The current open issue in the attention mechanism is the capability to explain and interpret the neural network. Thus, deep networks and transfer learning are integrated with the attention mechanism to examine the high-level features selection in NLP tasks.

Attention for Outlier Detection and Sample Weighing: The attention mechanism for outlier detection with dynamic sample weighing allows the selection of different training data in different training phases. Hence, attention-based outlier detection needs to implement in the future.

Unsupervised Learning With Attention: Exploiting unsupervised learning in attention mechanism is considered a long-term challenge; as such, it becomes a promising research direction due to the learning process of humans being largely unsupervised.

Model Evaluation in Attention Analysis: The analysis of the attention mechanism helps to measure the architecture in performing a task and assess the uncertainty as well as improve its interpretability. Hence, the model evaluation will be performed to validate the relevant information and strategy.

Neural-Symbolic Learning and Reasoning: The attention mechanism is started to instigate with symbolic neural models for NLP tasks remains in the early stage. Neural architectures governing attention for reasoning NLP tasks must also be addressed with symbolic approaches.