Language GANs Falling ShortLanguage GANs Falling ShortMassimo Caccia and Lucas Caccia and William Fedus and Hugo Larochelle and Joelle Pineau and Laurent Charlin2018
Paper summarydecodyngThis paper’s high-level goal is to evaluate how well GAN-type structures for generating text are performing, compared to more traditional maximum likelihood methods. In the process, it zooms into the ways that the current set of metrics for comparing text generation fail to give a well-rounded picture of how models are performing.
In the old paradigm, of maximum likelihood estimation, models were both trained and evaluated on a maximizing the likelihood of each word, given the prior words in a sequence. That is, models were good when they assigned high probability to true tokens, conditioned on past tokens. However, GANs work in a fundamentally new framework, in that they aren’t trained to increase the likelihood of the next (ground truth) word in a sequence, but to generate a word that will make a discriminator more likely to see the sentence as realistic. Since GANs don’t directly model the probability of token t, given prior tokens, you can’t evaluate them using this maximum likelihood framework.
This paper surveys a range of prior work that has evaluated GANs and MLE models on two broad categories of metrics, occasionally showing GANs to perform better on one or the other, but not really giving a way to trade off between the two.
- The first type of metric, shorthanded as “quality”, measures how aligned the generated text is with some reference corpus of text: to what extent your generated text seems to “come from the same distribution” as the original. BLEU, a heuristic frequently used in translation, and also leveraged here, measures how frequently certain sets of n-grams occur in the reference text, relative to the generated text. N typically goes up to 4, and so in addition to comparing the distributions of single tokens in the reference and generated, BLEU also compares shared bigrams, trigrams, and quadgrams (?) to measure more precise similarity of text.
- The second metric, shorthanded as “diversity” measures how different generated sentences are from one another. If you want to design a model to generate text, you presumably want it to be able to generate a diverse range of text - in probability terms, you want to fully sample from the distribution, rather than just taking the expected or mean value. Linguistically, this would be show up as a generator that just generates the same sentence over and over again. This sentence can be highly representative of the original text, but lacks diversity. One metric used for this is the same kind of BLEU score, but for each generated sentence against a corpus of prior generated sentences, and, here, the goal is for the overlap to be as low as possible
The trouble with these two metrics is that, in their raw state, they’re pretty incommensurable, and hard to trade off against one another. The authors of this paper try to address this by observing that all models trade off diversity and quality to some extent, just by modifying the entropy of the conditional token distribution they learn. If a distribution is high entropy, that is, if it spreads probability out onto more tokens, it’s likelier to bounce off into a random place, which increases diversity, but also can make the sentence more incoherent. By contrast, if a distribution is too low entropy, only ever putting probability on one or two words, then it will be only ever capable of carving out a small number of distinct paths through word space.
The below table shows a good example of what language generation can look like at high and low levels of entropy
The entropy of a softmax distribution be modified, without changing the underlying model, by changing the *temperature* of the softmax calculation. So, the authors do this, and, as a result, they can chart out that model’s curve on the quality/diversity axis. Conceptually, this is asking “at a range of different quality thresholds, how good is this model’s diversity,” and vice versa. I mentally analogize this to a ROC curve, where it’s not really possible to compare, say, precision of models that use different thresholds, and so you instead need to compare the curve over a range of different thresholds, and compare models on that.
When they do this for GANs and MLEs, they find that, while GANs might dominate on a single metric at a time, when you modulate the temperature of MLE models, they’re able to achieve superior quality when you tune them to commensurate levels of diversity.
Language GANs Falling Short
arXiv e-Print archive - 2018 via Local arXiv
First published: 2018/11/06 (7 months ago) Abstract: Generating high-quality text with sufficient diversity is essential for a
wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE)
models trained with teacher forcing have constantly been reported as weak
baselines, where poor performance is attributed to exposure bias; at inference
time, the model is fed its own prediction instead of a ground-truth token,
which can lead to accumulating errors and poor samples. This line of reasoning
has led to an outbreak of adversarial based approaches for NLG, on the account
that GANs do not suffer from exposure bias. In this work, we make several
surprising observations with contradict common beliefs. We first revisit the
canonical evaluation framework for NLG, and point out fundamental flaws with
quality-only evaluation: we show that one can outperform such metrics using a
simple, well-known temperature parameter to artificially reduce the entropy of
the model's conditional distributions. Second, we leverage the control over the
quality / diversity tradeoff given by this parameter to evaluate models over
the whole quality-diversity spectrum, and find MLE models constantly outperform
the proposed GAN variants, over the whole quality-diversity space. Our results
have several implications: 1) The impact of exposure bias on sample quality is
less severe than previously thought, 2) temperature tuning provides a better
quality / diversity trade off than adversarial training, while being easier to
train, easier to cross-validate, and less computationally expensive.