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Title: Connecting the Dots Between MLE and RL for Sequence Prediction

Paper content:
Sequence prediction models can be learned from example sequences with a variety of training algorithms.
Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time.
Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.
A rich set of other algorithms, such as data noising, RAML, and softmax policy gradient, have also been developed from different perspectives.
In this paper, we present a formalism of entropy regularized policy optimization, and show that the apparently distinct algorithms, including MLE, can be reformulated as special instances of the formulation.
The difference between them is characterized by the reward function and two weight hyperparameters.
The unifying interpretation enables us to systematically compare the algorithms side-by-side, and gain new insights into the trade-offs of the algorithm design.
The new perspective also leads to an improved approach that dynamically interpolates among the family of algorithms, and learns the model in a scheduled way.
Experiments on machine translation, text summarization, and game imitation learning demonstrate superiority of the proposed approach.
Sequence prediction problem is ubiquitous in many applications, such as generating a sequence of words for machine translation Sutskever et al., 2014 ), text summarization (Hovy & Lin, 1998; Rush et al., 2015) , and image captioning Karpathy & Fei-Fei, 2015) , or taking a sequence of actions to complete a task.
In these problems (e.g., Mnih et al., 2015; Ho & Ermon, 2016) , we are often given a set of sequence examples, from which we want to learn a model that sequentially makes the next prediction (e.g., generating the next token) given the current state (e.g., the previous tokens).
A standard training algorithm is based on supervised learning which seeks to maximize the loglikelihood of example sequences (i.e., maximum likelihood estimation, MLE).
Despite the computational simplicity and efficiency, MLE training can suffer from compounding error (Ranzato et al., 2016; Ross & Bagnell, 2010) in that mistakes at test time accumulate along the way and lead to states far from the training data.
Another line of approaches overcome the training/test discrepancy issue by resorting to the reinforcement learning (RL) techniques (Ranzato et al., 2016; Rennie et al., 2017) .
For example, Ranzato et al. (2016) used policy gradient (Sutton et al., 2000) to train a text generation model with the task metric (e.g., BLEU) as reward.
However, RL-based approaches can face challenges of prohibitively poor sample efficiency and high variance.
To this end, a diverse set of methods has been developed that is in a middle ground between the two paradigms of MLE and RL.
For example, RAML adds reward-aware perturbation to the MLE data examples; SPG (Ding & Soricut, 2017) leverages reward distribution for effective sampling of policy gradient.
Other approaches such as data noising (Xie et al., 2017 ) also show improved results.
In this paper, we establish a unifying perspective of the above distinct learning algorithms.
Specifically, we present a generalized entropy regularized policy optimization framework, and show that the diverse algorithms, such as MLE, RAML, data noising, and SPG, can all be re-formulated as special cases of the framework, with the only difference being the choice of reward and the values of two weight hyperparameters (Figure 1 ).
In particular, we show MLE is equivalent to using a Delta-function reward which returns 1 to model samples that match training examples exactly, and −∞ to any other samples.
Such extremely restricted reward has literally disabled any exploration of the model beyond training data, yielding brittle prediction behaviors.
Other algorithms essentially use various locally-relaxed rewards, joint with the model distribution, for broader (and more costly) exploration during training.
Besides the new views of the existing algorithms, the unifying perspective also leads to new algorithms for improved learning.
We develop interpolation algorithm, which, as training proceeds, gradually expands the exploration space by annealing both the reward function and the weight hyperparameters.
The annealing in effect dynamically interpolates among the existing algorithms from left to right in Figure 1 .
We conduct experiments on the tasks of text generation including machine translation and text summarization, and game imitation learning.
The interpolation algorithm shows superior performance over various previous methods.
We have presented a unifying perspective of a variety of learning algorithms for sequence prediction problems.
The framework is based on a generalized entropy regularized policy optimization formulation, and we show the distinct algorithms are equivalent to specifying the reward and weight hyperparameters.
The new consistent treatment provides systematic understanding and comparison across the algorithms, and inspires further improved learning.
The proposed interpolation algorithm shows consistent improvement in machine translation, text summarization, and game imitation learning.
Ranzato et al. (2016) made an early attempt to address the exposure bias problem by exploiting the policy gradient algorithm (Sutton et al., 2000) .
Policy gradient aims to maximizes the expected reward:
where RP G is usually a common reward function (e.g., BLEU).
Taking gradient w.r.t θ gives:
We now reveal the relation between the ERPO framework we present and the policy gradient algorithm.
Starting from the M-step of Eq.(2) and setting (α = 1, β = 0) as in SPG (section ??), we use p θ n as the proposal distribution and obtain the importance sampling estimate of the gradient (we omit the superscript n for notation simplicity):
Eq [∇ θ log p θ (y)] = Ep θ q(y) p θ (y) ∇ θ log p θ (y) = 1/Z θ · Ep θ exp{R(y|y * )} · ∇ θ log p θ (y) ,
where Z θ = y exp{log p θ + R} is the normalization constant of q, which can be considered as adjusting the step size of gradient descent.
We can see that Eq.(11) recovers Eq.(10) if we further set R = log RP G, and omit the scaling factor Z θ . In
other words, policy gradient can be seen as a special instance of the general ERPO framework with (R = log RP G, α = 1, β = 0) and with Z θ omitted.
The MIXER algorithm (Ranzato et al., 2016) incorporates an annealing strategy that mixes between MLE and policy gradient training.
Specifically, given a ground-truth example y * , the first m tokens y * 1:m are used for evaluating MLE loss, and starting from step m + 1, policy gradient objective is used.
The m value decreases as training proceeds.
With the relation between policy gradient and ERPO as established above, MIXER can be seen as a specific instance of the proposed interpolation algorithm (section 4) that follows a restricted annealing strategy for token-level hyperparameters (λ1, λ2, λ3).
That is, for t < m in Eq.4 (i.e.,the first m steps), (λ1, λ2, λ3) is set to (0, 0, 1) and c = 1, namely the MLE training; while for t > m, (λ1, λ2, λ3) is set to (0.5, 0.5, 0) and c = 2.

assistant: text: An entropy regularized policy optimization formalism subsumes a set of sequence prediction learning algorithms. A new interpolation algorithm with improved results on text generation and game imitation learning.