Incorporating Copying Mechanism in Sequence-to-Sequence Learning (1603.06393v3)

Abstract: We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or even long phrases in conversation. The challenge with regard to copying in Seq2Seq is that new machinery is needed to decide when to perform the operation. In this paper, we incorporate copying into neural network-based Seq2Seq learning and propose a new model called CopyNet with encoder-decoder structure. CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Our empirical study on both synthetic data sets and real world data sets demonstrates the efficacy of CopyNet. For example, CopyNet can outperform regular RNN-based model with remarkable margins on text summarization tasks.

• The paper's main contribution is the CopyNet model that integrates copying into Seq2Seq learning, effectively addressing limitations in handling out-of-vocabulary words.
• It introduces a dual mechanism combining attention-based content addressing with location-based selective reads, significantly enhancing performance on synthetic, summarization, and dialogue tasks.
• Empirical results demonstrate considerable improvements in ROUGE scores and accuracy, showcasing CopyNet’s robust performance over traditional models.

Incorporating Copying Mechanism in Sequence-to-Sequence Learning

The research presented by Gu et al. tackles the prevalent issue in sequence-to-sequence (Seq2Seq) learning known as copying, where particular segments in the input sequence need to be replicated in the output sequence. This phenomenon is frequently observed in human language communication, such as repetition of named entities or specific phrases. The challenge within traditional neural network-based Seq2Seq models is that they lack the machinery to effectively manage this copying process. The authors propose a novel model named CopyNet, which integrates copying into the standard Seq2Seq framework, creating a sophisticated encoder-decoder architecture capable of both generating words and copying sub-sequences from the input.

Technical Approach

The primary innovation in CopyNet is its ability to merge the generation and copying processes within a single model. CopyNet uses a probabilistic mixture model that dynamically decides between generating new words and copying existing ones from the input sequence. This decision is made based on the decoder state and the context provided by the encoder.

The encoder in CopyNet operates as a bi-directional RNN, transforming the input sequence into a certain representation. The decoder, also an RNN, then reads this representation and generates the output sequence. Key to CopyNet's functionality is its dual approach to addressing memory: it includes both content-based addressing, achieved through the attention mechanism, and location-based addressing, achieved through selective read operations. This hybrid addressing strategy allows CopyNet to both understand the content and maintain a precise reference to the location of the copied segments in the source.

Empirical Results

The efficacy of CopyNet is demonstrated across three different tasks: synthetic datasets, text summarization, and single-turn dialogue systems.

1. Synthetic Dataset: The model was tested on artificially generated sequences designed to mimic rote memorization patterns. CopyNet achieved significantly higher accuracy in generating output sequences compared to traditional RNN encoder-decoder models and attention-based models. For transformation rules involving both copying and non-copying operations, CopyNet substantially outperformed other models.
2. Text Summarization: Utilizing the LCSTS dataset, CopyNet demonstrated notable improvements in ROUGE-1, ROUGE-2, and ROUGE-L scores over existing RNN-based techniques, both with and without attention mechanisms. The model's ability to handle out-of-vocabulary (OOV) words by copying them directly from the input was particularly advantageous, yielding fluent summaries even when the vocabulary was limited.
3. Single-turn Dialogue: On datasets derived from Baidu Tieba, CopyNet significantly outperformed RNN- and attention-based models in generating appropriate responses, particularly when dealing with OOV entities. In a more stringent test where the training and test sets had no overlapping entities, CopyNet's accuracy in replicating crucial segments from the input sequence highlighted its robust handling of unseen data.

Implications and Future Directions

The primary contribution of CopyNet lies in its sophisticated handling of the copying mechanism within the Seq2Seq framework. By introducing a model that seamlessly integrates content generation and copying, this research potentially impacts various NLP applications, including machine translation, dialogue systems, and text summarization, particularly those challenged by extensive vocabularies and OOV issues.

Theoretically, CopyNet provides a framework for further exploration into hybrid addressing mechanisms in neural networks. Its dual approach of content and location-based addressing sets a precedent for future models aiming to balance understanding with rigid memorization needs effectively.

Looking ahead, extending the CopyNet model to tasks involving heterogeneous source and target sequences, such as machine translation, offers a promising avenue for research. Enhancing its capability to handle references beyond the input sequence could provide substantial improvements in multi-modal and cross-domain applications.

In summary, by embedding a copying mechanism within the Seq2Seq learning paradigm, CopyNet addresses fundamental limitations in current models and offers a robust solution poised to enhance performance across various natural language processing tasks.