Long Short-Term Memory-Networks for Machine Reading (1601.06733v7)

Abstract: In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a machine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory and attention. The reader extends the Long Short-Term Memory architecture with a memory network in place of a single memory cell. This enables adaptive memory usage during recurrence with neural attention, offering a way to weakly induce relations among tokens. The system is initially designed to process a single sequence but we also demonstrate how to integrate it with an encoder-decoder architecture. Experiments on LLMing, sentiment analysis, and natural language inference show that our model matches or outperforms the state of the art.

• The paper presents a novel LSTM-network architecture that integrates memory networks with attention to capture long-term dependencies.
• It shows improved performance across language modeling, sentiment analysis, and natural language inference with metrics like lower perplexity and higher accuracy.
• The study highlights how memory-augmented LSTMs can overcome traditional RNN limitations in processing structured text inputs.

Long Short-Term Memory-Networks for Machine Reading

The paper "Long Short-Term Memory-Networks for Machine Reading" by Jianpeng Cheng, Li Dong, and Mirella Lapata proposes an innovative approach aimed at addressing the limitations of sequence-level networks, particularly Long Short-Term Memory (LSTM) networks, when processing structured text input. This manuscript delineates the development of a reading simulator designed to perform shallow reasoning with memory and attention mechanisms while processing text incrementally.

Key Contributions

The paper introduces a Long Short-Term Memory-Network (LSTMN) architecture that augments the traditional LSTM by incorporating a memory network module. The memory network stores contextual representations of input tokens, enabling adaptive memory usage during the LSTM's recurrence. This design facilitates the induction of token relations through a neural attention layer, enhancing the model's ability to memorize longer sequences more efficiently compared to conventional LSTMs.

Technical Details

1. Memory Network Integration: The LSTMN replaces the single memory cell in the standard LSTM with a memory network, encapsulating a series of memory slots. Each token is associated with a unique memory slot, and an attention mechanism is employed to address these memories dynamically. This allows for more flexible and precise handling of long-term dependencies.
2. Attention Mechanism: The attention mechanism is utilized to compute relations between the current and previous tokens, deriving a probability distribution over past hidden state vectors. This facilitates creating adaptive summary vectors from the hidden and memory tapes, which are then utilized for memory addressing and updating the token representations in a non-Markovian manner.
3. Sequence-to-Sequence Modeling: The LSTMN model is extended to handle dual-sequence tasks by integrating it into an encoder-decoder architecture. Both shallow and deep attention fusion methods are explored for combining intra- and inter-attention mechanisms. Deep fusion, in particular, allows for the recurrence of inter-attention vectors in the decoder, further refining the representation of source information.

Experimental Results

The efficacy of the LSTMN was empirically validated across three tasks: LLMing, sentiment analysis, and natural language inference.

1. LLMing: The model demonstrated superior performance on the Penn Treebank dataset, achieving a perplexity of 102 with a three-layer LSTMN. This outperformed several strong baselines, including traditional LSTMs and more advanced architectures such as the gated-feedback LSTM (gLSTM) and depth-gated LSTM (dLSTM).
2. Sentiment Analysis: On the Stanford Sentiment Treebank, both fine-grained and binary classification tasks were performed. The two-layer LSTMN achieved an accuracy of 87.0% on binary classification, which was competitive with top-performing models such as the T-CNN while exceeding standard LSTM variants.
3. Natural Language Inference: In the SNLI dataset, the LSTMN with deep attention fusion achieved an accuracy of 86.3%, surpassing models like the matching LSTM (mLSTM) and demonstrating the model's capability in recognizing textual entailment and relationship extraction.

Implications and Future Directions

The LSTMN framework presents a significant step towards leveraging memory and attention mechanisms to improve the processing of structured text in neural networks. The incorporation of a memory network enables better handling of longer sequences and structured input, addressing inherent limitations seen in traditional RNNs and LSTMs.

From a theoretical perspective, integrating memory networks and attention strategies within the LSTM framework shows promise in understanding and leveraging sequence structures more effectively. Practically, this approach can enhance various natural language processing tasks requiring detailed comprehension and long-term dependency resolution.

Looking forward, further research endeavors could explore adapting the LSTMN architecture for tasks such as dependency parsing and relation extraction, particularly when direct supervision is available. Also, developing more complex architectures capable of reasoning over nested or hierarchical structures could offer even richer modeling capabilities, potentially leading to advancements in domains demanding deep linguistic and semantic understanding.

In conclusion, the introduction of Long Short-Term Memory-Networks marks an important contribution to the field of machine reading, presenting a well-architected blend of memory networks and attention mechanisms to elevate the performance and capability of neural models in understanding and processing structured input.