Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation.
The dominant parametric approach is based on locally normalized models which predict one word at a time.
While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process.
In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level.
In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation.
Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa.
Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines.
Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.
The dominant approach to parametric text generation is based on large neural auto-regressive models (Radford et al., 2019) .
These models can be trained efficiently via maximum likelihood and they can efficiently generate samples of remarkable quality.
Key to their success is local normalization, i.e. they are defined in terms of a product of conditional distributions, one for each token in the sequence.
Such distributions are relatively cheap to compute with modern hardware given the limited vocabulary size of common sub-word units like BPE (Sennrich et al., 2015) .
Unfortunately, local normalization also brings some drawbacks.
First, the designer of the model needs to specify the order in which tokens are generated.
Second, at training time the model is conditioned on ground truth context while at test time it is conditioned on its own generations, a discrepancy referred to as exposure bias (Ranzato et al., 2016) .
Finally, while heuristics like beam search somewhat help rescore at the sequence level, generation generally lacks long-range coherency because it is produced by the greedy selection of one token at the time without lookahead.
Energy-based models (EBMs) (Hinton, 2002; LeCun et al., 2006; Ranzato et al., 2007 ) are a more general framework which potentially address all these issues, as they do not require any local normalization.
They only require the definition of an energy function defined over the whole input sequence.
Training aims at shaping the energy function such that regions of high density of training data points have lower energy than elsewhere.
In principle, EBMs are ideal for modeling text as they can score the whole input at once, they are not prone to label bias (Bottou, 1991) and they may enable generation of large chunks of text, which should help improve coherency.
However, so far EBMs had limited application in text generation, because sampling from the model is intractable, and so is maximum likelihood training.
The problem is that shaping the energy function is accomplished by updating the model parameters such that the energy is decreased at the training data points (a.k.a. positive examples) and increased at other data points (a.k.a. negative examples).
In maximum likelihood training negatives are generated from the model, but in text application we cannot use gradient-based MCMC methods (Teh et al., 2003; Du & Mordatch, 2019) and Gibbs sampling (Welling et al., 2005) is too slow to be practical.
Generating negatives by local perturbations of the ground truth would be efficient but hardly useful for generation purposes, when at test time the model needs to generate from scratch.
Recently, Bakhtin et al. (2019) carefully studied the problem of training a discriminator to distinguish human written text from language model generations.
They experimented with different language model and discriminator architectures, training/test time corpora and concluded that the discriminator can generalize rather well to weaker language models when the training/test corpora match.
Bakhtin et al. (2019) found that the learned discriminator is not robust to random perturbations, and argued that the discriminator operates in the "residual" space of the language model.
Concurrently, Grover et al. (2019) proposed a general approach to "de-bias" a generator, by simply training a discriminator and using its output for importance sampling.
In this work, we build upon these two works.
First, we formalize the residual interpretation by Bakhtin et al. (2019) and use a generative model of the form:
where P LM (x) is a locally normalized language model which is fixed during training, and E θ is the energy function parameterized by θ.
The resulting model P θ (x) is globally normalized due to the energy term.
Note that the same residual formulation was also used in Rosenfeld et al. (2001) ; Wang & Ou (2018b) ; Parshakova et al. (2019) .
This formulation has multi-fold benefits.
First, by incorporating a locally normalized language model, we can leverage recent advancements in locally normalized language modeling.
Second, the language model provides a natural proposal distribution for training (Bakhtin et al., 2019) as we shall see in §3.
Third, training can be made efficient by using the conditional noise contrastive estimation objective (Gutmann & Hyvärinen, 2010) .
Lastly, this formulation also enables efficient evaluation and generation via importance sampling (Horvitz & Thompson, 1952; Grover et al., 2019) .
In some sense, this last point is perhaps the central contribution of the paper, as it allows estimating perplexity of the residual EBM, and thus allows these EBMs to be compared in a standard way to other models.
Indeed, in §4 we show that our joint model decreases perplexity on two large datasets, when compared to various auto-regressive language model baselines.
Finally, the EBM generations are significantly preferred by humans according to our qualitative evaluation.
To the best of our knowledge, this is the first time that an EBM has demonstrated improved generation ability against very strong auto-regressive baselines, both in terms of estimated perplexity and through human evaluation.
We investigated an EBM trained on the residual of a pretrained autoregressive language model (Wang & Ou, 2018b; Parshakova et al., 2019) .
The resulting joint model scores sequences holistically, thanks to the energy function.
Training is very efficient and consists of a binary classification task between positives from the training set and pregenerated negatives from the fixed language model.
Generation is also very efficient as it amounts to resampling from the large set of negatives produced by the base language model.
Our estimates show that the resulting model has lower perplexity than the base language model.
Finally, this approach may be interpreted as a natural way to finetune a large bidirectional transformer like BERT for text generation applications.
In the future, we plan to investigate other ways to generate negatives that may strike a better tradeoff between the amount of compute each negative requires and their closeness to the joint model distribution.
It would also be interesting to explore other loss functions and the generation of longer pieces of text by using this model auto-regressively at the chunk level, as opposed to the token level.
A APPENDIX
We show that Energy-Based models when trained on the residual of an auto-regressive language model can be used effectively and efficiently to generate text.