(2017. 11) Neural Text Generation

published in 2017. 11
Ziang Xie

Simple Summary

Practivate guide for diagnosing and resolving pathological behavior during decoding.
The primary focus is on tasks where the target is a single sentence hence the term "text generation" as opposed to "language generation".

Preprocessing

Ultimately, if using word tokens, it's important to use a consistent tokenization scheme for all inputs to the system - this includes handling of contractions, punctuation marks such as quotes and hyphens, periods denoting abbrevations (nonbreaking prefixes) vs. sentence boundaries, character escaping, etc.

Model

RNN
- use shared parameter matrices across different time steps and combine the input at the current time step with the previous hidden state summarizing all previous time steps.
CNN
- Convolutions with kernels reused across timesteps can also be used with masking to avoid peeking ahead at future inputs during trainig.
Attention
- Attention mechanism acts as a shortcut connection between the target output prediction and the relevant source input hidden states.

Training

Optimize over the model parameters θ the sequence cross-entropy loss.
recent research has also explored other methods
- using RL or separate adversarial loss
  - Adversarial Learning for Neural Dialogue Generation (2017. 1)
  - Wasserstein gan (2017. 1)
Useful heuristics
- Sort the next dozen or so batches of sentences by length
- training set is small, tuning regularization
- Measure validation loss after each epoch and anneal the learning rate when validation loss stop decreasing.
- Periodically checkpoint model parameters and measure downstream performance.
- Ensembling almost always improves performance.

Decoding

During decoding, we are given the source sentence X and seek to generate the target Y^ that maximizes some scoring function s(Y^)^2.
Beam Search
- surprising result with neural models is that relatively small beam size yield good results with rapidly dimishing return. Further, larger beam sizes can event yield (slightly) worse results.
When measuring the score function, Useful metrics include:
- Average length of decoded outputs Y^ vs average length of reference targets Y.
- s(Y^) vs s(Y), then inspecting the ratio s(Y^)/s(Y). If the average ratio is especially low, then there may be a bug in the beam search, or the beam size may need to be increased. If the average ratio is high, then the scoring fuction may not be appropriate.
- For some applications computing edit distance (insertions, substitutions, deletions) between Y^ and Y my also be useful.

Common issues.

Rare and out-of-vocabulary (OOV) words
- [Neural machine translation of rare words with subword
  units](https://arxiv.org/abs/1508.07909) (2015. 8)
- Google’s neural machine translation system: Bridging the gap between human and machine translation (2016 .9)
Docoded output short, truncated or ignore portions of input
- normalizing the log-probability score and ading ad length bonus.
- Modeling coverage for neural machine translation (2016. 1)
- Google’s neural machine translation system: Bridging the gap between human and machine translation (2016 .9)
Decoded output repeats
- easily detected using the attention matrix A with some manually selected threshold.
- [Get to the point: Summarization with pointer-generator
  networks](https://arxiv.org/abs/1704.04368) (2017. 4)
Lack of diversity
- Increasing the temperature τ of the softmax exp(zi/τ)/ Sigma_j exp(zj/τ) is a simple method for trying to encourage more diversity in decoded outputs.
- A Diversity-Promoting Objective Function for Neural Conversation Models (2015. 10)
- A simple, fast diverse decoding algorithm for neural generation (2016. 11)

Attention

basic attention mechanism used to "attend" to portions of the encoder hidden states during each decoder timestep has many extensions and applications.

Evaluation

Common metric based n-gram
- [ROUGE](https://en.wikipedia.org/wiki/ROUGE_(metric))
- BLUE
Need metric
- Why We Need New Evaluation Metrics for NLG (2017. 6)

Previous(2017. 10) Multi Paragraph RC Next(2017. 12) Contextualized Word For RC

Last updated 6 years ago

Simple Summary

Practivate guide for diagnosing and resolving pathological behavior during decoding.

The primary focus is on tasks where the target is a single sentence hence the term "text generation" as opposed to "language generation".

Preprocessing

Ultimately, if using word tokens, it's important to use a consistent tokenization scheme for all inputs to the system - this includes handling of contractions, punctuation marks such as quotes and hyphens, periods denoting abbrevations (nonbreaking prefixes) vs. sentence boundaries, character escaping, etc.

Model

RNN

use shared parameter matrices across different time steps and combine the input at the current time step with the previous hidden state summarizing all previous time steps.

CNN

Convolutions with kernels reused across timesteps can also be used with masking to avoid peeking ahead at future inputs during trainig.

Attention

Attention mechanism acts as a shortcut connection between the target output prediction and the relevant source input hidden states.

Training

Optimize over the model parameters θ the sequence cross-entropy loss.

recent research has also explored other methods

using RL or separate adversarial loss
- Adversarial Learning for Neural Dialogue Generation (2017. 1)
- Wasserstein gan (2017. 1)

Useful heuristics

Sort the next dozen or so batches of sentences by length
training set is small, tuning regularization
Measure validation loss after each epoch and anneal the learning rate when validation loss stop decreasing.
Periodically checkpoint model parameters and measure downstream performance.
Ensembling almost always improves performance.

Decoding

During decoding, we are given the source sentence X and seek to generate the target Y^ that maximizes some scoring function s(Y^)^2.

Beam Search

surprising result with neural models is that relatively small beam size yield good results with rapidly dimishing return. Further, larger beam sizes can event yield (slightly) worse results.

When measuring the score function, Useful metrics include:

Average length of decoded outputs Y^ vs average length of reference targets Y.
s(Y^) vs s(Y), then inspecting the ratio s(Y^)/s(Y). If the average ratio is especially low, then there may be a bug in the beam search, or the beam size may need to be increased. If the average ratio is high, then the scoring fuction may not be appropriate.
For some applications computing edit distance (insertions, substitutions, deletions) between Y^ and Y my also be useful.

Common issues.

Attention

basic attention mechanism used to "attend" to portions of the encoder hidden states during each decoder timestep has many extensions and applications.

Evaluation