What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment (1509.00838v2)

Abstract: We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small subset of salient records to talk about, and finally employs a decoder to generate free-form descriptions of the aligned, selected records. Our model achieves the best selection and generation results reported to-date (with 59% relative improvement in generation) on the benchmark WeatherGov dataset, despite using no specialized features or linguistic resources. Using an improved k-nearest neighbor beam filter helps further. We also perform a series of ablations and visualizations to elucidate the contributions of our key model components. Lastly, we evaluate the generalizability of our model on the RoboCup dataset, and get results that are competitive with or better than the state-of-the-art, despite being severely data-starved.

• The paper presents a novel neural model that integrates content selection with text generation, achieving up to a 59% improvement in BLEU scores.
• It employs a bidirectional LSTM encoder and a distinctive coarse-to-fine aligner to efficiently focus on the most relevant records.
• The model demonstrates robust cross-domain performance by delivering competitive results on both large-scale (WeatherGov) and data-starved (RoboCup) datasets.

Selective Generation using LSTMs with Coarse-to-Fine Alignment: A Summary

The paper "What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment" by Hongyuan Mei, Mohit Bansal, and Matthew R. Walter discusses an innovative neural architecture for the task of selective generation. Selective generation—comprised of content selection and surface realization—offers the potential to transform rich databases of event records into coherent natural language descriptions. Historically, approaches have tackled these two subtasks separately, often relying on domain-specific resources, thereby limiting cross-domain efficiency and coherence. This paper introduces an end-to-end neural approach that jointly learns content selection and surface realization without specialized linguistic resources or templates.

Methodological Contributions

The authors propose a neural encoder-aligner-decoder model structured around LSTM-based recurrent neural networks (RNNs). The distinctive feature of this model is its coarse-to-fine aligner, which effectively narrows down the over-determined input data to a smaller subset of salient records for discussion. The process unfolds through three major components:

1. Encoder: Utilizes a bidirectional LSTM-RNN to encode the full set of records, capturing dependencies among records which aids in determining relevance to the natural language output.
2. Coarse-to-Fine Aligner: Implements a unique two-stage selection mechanism—a pre-selector estimates a prior probability of selecting each record, while the refiner refines these probabilities in conjunction with alignment weights, directing focus to the most relevant records.
3. Decoder: Computes the word likelihood for generating natural language descriptions by leveraging LSTM units capable of capturing long-range dependencies inherent in the data.

Experimental Validation

The model was benchmarked on the WeatherGov dataset, achieving a substantial improvement in both content selection and generation tasks, with up to a 59% relative improvement in generation as measured by BLEU scores. The efficacy of the coarse-to-fine aligner was evident as it avoided exhaustive searches over input records, thereby enhancing performance without necessitating specialized features.

Furthermore, to illustrate its generalizability, the model was evaluated on the RoboCup dataset, which is smaller in size and represents a different domain entirely. Even when severely data-starved, the model performed competitively against state-of-the-art approaches, reinforcing the cross-domain applicability of the proposed architecture.

Implications and Future Directions

The architecture's performance across domains without the dependency on domain-specific features presents a robust potential for broader applications. The model's ability to perform selective generation directly from raw data-text pairs opens avenues for automation in fields reliant on database-driven narrations, such as weather broadcasting, sports commentary, and possibly even medical report generation.

For future developments, further research could explore adaptations of this model to non-sequential data or apply it to domains with even finer-grained selection criteria. Additionally, examining the encoder's performance in varying contextual dependency scenarios could yield insights for enhancing modeling of complex inter-record dependencies.

In summary, by presenting a novel method for selective generation using LSTMs with a coarse-to-fine alignment mechanism, the paper contributes significantly to neural architecture frameworks capable of handling the dual complexity of selection and realization, promising further advancements in auto-narrative technology solutions.