Original: CaptainGAN: Navigate Through Embedding Space For Better Text Generation
Revised: CaptainGAN: Navigate Through Embedding Space For Better Text Generation

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: Abstract
Revised: Abstract

Original: Score-function-based text generation approaches such as REINFORCE, in general, suffer from high computational complexity and training instability problems.
Revised: Score-function-based text generation approaches such as REINFORCE, in general, suffer from high computational complexity and training instability problems.

Original: This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a reward function and ignore the gradient information.
Revised: This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a reward function and ignore the gradient information.

Original: In this paper, we propose a novel approach, CaptainGAN, which adopts the straight-through gradient estimator and introduces a ”re-centered” gradient estimation technique to steer the generator toward better text tokens through the embedding space.
Revised: In this paper, we propose a novel approach, CaptainGAN, which adopts the straight-through gradient estimator and introduces a ”re-centered” gradient estimation technique to steer the generator toward better text tokens through the embedding space.

Original: Our method is stable to train and converges quickly without maximum likelihood pre-training.
Revised: Our method is stable to train and converges quickly without maximum likelihood pre-training.

Original: On multiple metrics of text quality and diversity, our method outperforms existing GAN-based methods on natural language generation
Revised: On multiple metrics of text quality and diversity, our method outperforms existing GAN-based methods on natural language generation.

Original:  Introduction
Revised:  Introduction

Original: Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) have led to many advances in image generation, image editing, style transfer, and representation learning (Karras et al., 2017;
Revised: Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) have led to many advances in image generation, image editing, style transfer, and representation learning (Karras et al., 2017;

Original: Brock et al., 2018; Karras et al., 2019).
Revised: Brock et al., 2018; Karras et al., 2019).

Original: Unsurprisingly, much effort has been devoted to adopting the GAN framework for unsupervised text generation (Yu et al., 2017;  Balagopalan et al., 2018; Fedus et al., 2018; Guo et al., 2018; de Masson d’Autume et al., 2019;
Revised: Unsurprisingly, much effort has been devoted to adopting the GAN framework for unsupervised text generation (Yu et al., 2017; Che et al., 2017; Balagopalan et al., 2018; Fedus et al., 2018; Guo et al., 2018; de Masson d’Autume et al., 2019;

Original: Zhang et al., 2017; Nie et al., 2019).
Revised: Zhang et al., 2017; Nie et al., 2019).

Original: However, as finding a Nash equilibrium is not as straightforward as finding a local optima, researchers have been forced to develop many ad-hoc tricks and techniques to make GAN training well-behaved.
Revised: However, as finding a Nash equilibrium is not as straightforward as finding a local optima, researchers have been forced to develop many ad-hoc tricks and techniques to make GAN training well-behaved.

Original: In the text generation setting, they are also faced with the additional obstacle of passing discrete tokens through a non-differentiable operation, which prohibits back-propagating the gradient signal to the generator.
Revised: In the text generation setting, they are also faced with the additional obstacle of passing discrete tokens through a non-differentiable operation, which prohibits back-propagating the gradient signal to the generator.

Original: To address the issue of non-differentiablity, researchers and practitioners use score function gradient estimators such as REINFORCE to train GANs for text generation, where the discriminator is cast as a reward function for the generator.
Revised: To address the issue of non-differentiablity, researchers and practitioners use score function gradient estimators such as REINFORCE to train GANs for text generation, where the discriminator is cast as a reward function for the generator.

Original: However, these methods still suffer from poor sample efficiency due to the credit assignment problem.
Revised: However, these methods still suffer from poor sample efficiency due to the credit assignment problem.

Original: We argue that it is disadvantageous to utilize the discriminator as simply a reward function when it is known that gradient-based backpropagation is a far more efficient way to perform credit assignment.
Revised: We argue that it is disadvantageous to utilize the discriminator as simply a reward function when it is known that gradient-based backpropagation is a far more efficient way to perform credit assignment.

Original: In this paper, we propose a novel unsupervised text generation technique, called CaptainGAN, which propagates a modified gradient signal from discriminator to generator in order to improve the efficiency and accuracy of the estimator.
Revised: In this paper, we propose a novel unsupervised text generation technique, called CaptainGAN, which propagates a modified gradient signal from discriminator to generator in order to improve the efficiency and accuracy of the estimator.

Original: Our contributions are as follows:
Revised: Our contributions are as follows:

Original: • An update procedure for the generator to incorporate gradient information from the discriminator during generator training.
Revised: • An update procedure for the generator to incorporate gradient information from the discriminator during generator training.

Original: • Lower memory and computational requirements than other RL-based counterparts.
Revised: • Lower memory and computational requirements than other RL-based counterparts.

Original: • Near SOTA results without maximum likelihood pretraining.
Revised: • Near SOTA results without maximum likelihood pretraining.

Original:  Background
Revised:  Background

Original: The Generative Adversarial Network (GAN) proposed in Goodfellow et al.
Revised: The Generative Adversarial Network (GAN) proposed in Goodfellow et al.

Original: (2014) is an innovative approach to the generative modeling problem.
Revised: (2014) is an innovative approach to the generative modeling problem.

Original: Rather than using the maximum likelihood estimation (MLE) directly to learn a probabilistic model, the GAN is a two-player minimax game in which the goal of one player, the generator G (cid:18) , is to generate samples ^ x from p (cid:18) , and the goal of the other player, the discriminator D ϕ , is to learn to classify whether or not a sample was generated from real data p data or the generator.
Revised: Rather than using the maximum likelihood estimation (MLE) directly to learn a probabilistic model, the GAN is a two-player minimax game in which the goal of one player, the generator G (cid:18) , is to generate samples ^ x from p (cid:18) , and the goal of the other player, the discriminator D ϕ , is to learn to classify whether or not a sample was generated from real data p data or the generator.

Original: [Equation]
Revised: [Equation]

Original: where V D and V G are respectively the objective functions of the discriminator D ϕ and the generator G (cid:18) .
Revised: where V D and V G are respectively the objective functions of the discriminator D ϕ and the generator G (cid:18) .

Original: Equation 2 is the alternative generator loss suggested by the original work as its gradient does not vanish when D ϕ (^ x ) is small.
Revised: Equation 2 is the alternative generator loss suggested by the original work as its gradient does not vanish when D ϕ (^ x ) is small.

Original: In the standard GAN architecture, the generator’s output is directly connected as the input to the discriminator in a fully differentiable manner, which means the gradients from the discriminator’s loss function can be back-propagated to the parameters of the generator.
Revised: In the standard GAN architecture, the generator’s output is directly connected as the input to the discriminator in a fully differentiable manner, which means the gradients from the discriminator’s loss function can be back-propagated to the parameters of the generator.

Original: However, it requires sampling a sequence of tokens from a discrete distribution in text generation, which is essentially non-differentiable.
Revised: However, it requires sampling a sequence of tokens from a discrete distribution in text generation, which is essentially non-differentiable.

Original: In order to avoid the intractability of the gradients, text GANs resort to some sort of estimation .
Revised: In order to avoid the intractability of the gradients, text GANs resort to some sort of estimation.

Revised: 2.1 Continuous Relaxation

Revised: Continuous relaxation approaches such as Gumbel-Softmax (Jang et al., 2016), approximates a stochastic categorical distribution in terms of the deterministic continuous function, gain much of popularity in recent years when dealing with the non-differentiability of discrete sampling (Kusner & Hernández-Lobato, 2016).

Revised: However, it poses serious issues.

Revised: For example, the discriminator can easily tell the difference between probabilistic fake data and discrete real data.

Revised: Furthermore, the way of generating data is inconsistent during training and testing phase.

Original: 2.1 Score-Function Gradient Estimator
Revised: 2.2 Score-Function Gradient Estimator

Original: The score function gradient estimator (Fu, 2006), or likelihood-ratio gradient estimator (Glynn, 1990), also known as the REINFORCE (Williams, 1992) is a common solution for non-differentiable issue as mentioned above.
Revised: The score function gradient estimator (Fu, 2006; Glynn, 1990) , also known as the REINFORCE (Williams, 1992) is a common solution for non-differentiable issue as mentioned above.

Original: Applying the REINFORCE algorithm, the gradient of the expectation of reward function f ϕ( x ) can be written as
Revised: Applying the REINFORCE algorithm, the gradient of the expectation of reward function f ϕ can be written as

Original: [Equation]
Revised: [Equation]

Original: Since it does not require f ϕ to be differentiable or even continuous as a function of x , the gradient of E ^ x (cid:24) p (cid:18) [ f ϕ (^ x )] can be back-propagated to the generator G (cid:18) .
Revised: Since it does not require f ϕ to be differentiable or even continuous as a function of x , the gradient of E ^ x (cid:24) p (cid:18) [ f ϕ (^ x )] can be back-propagated to the generator G (cid:18) .

Original: In the context of
Revised: In the context of

Original: GANs, E ^ x (cid:24) p (cid:18) [ f ϕ (^ x )] can be seen as the objective function of the generator and the reward function f ϕ can be replaced with D ϕ .
Revised: GANs, E ^ x (cid:24) p (cid:18) [ f ϕ (^ x )] can be seen as the objective function of the generator and the reward function f ϕ can be replaced with D ϕ .

Original: Although REINFORCE is an unbiased estimator, it still has a number of disadvantages such as high variance, low sample efficiency and the credit assignment problem.
Revised: Although REINFORCE is an unbiased estimator, it still has a number of disadvantages such as high variance, low sample efficiency and the credit assignment problem.

Original: Therefore, much effort is devoted to reducing the variance using special methods (Gu et al., 2016; Grathwohl et al., 2017), or to providing more dense rewards such as in Yu et al.
Revised: Therefore, much effort is devoted to reducing the variance using special methods (Gu et al., 2016; Grathwohl et al., 2017), or to providing more dense rewards such as in Yu et al.

Original: ; Fedus et al.
Revised: ; Che et al.

Revised: (2017); Fedus et al.

Original: (2018); de Masson d’Autume et al.
Revised: (2018); de Masson d’Autume et al.

Original: (2019), where Monte-Carlo roll-outs are used to obtain per-word rewards.
Revised: (2019), where Monte-Carlo roll-outs are used to obtain per-word rewards.

Original: 2.2 Straight-Through Gradient Estimator
Revised: 2.3 Straight-Through Gradient Estimator

Original: Another approach is the straight-through gradient estimator (Bengio et al., 2013; Jang et al., 2016).
Revised: Another approach is using a straight-through gradient estimator (Bengio et al., 2013; Jang et al., 2016).

Original: The basic idea is to perform non-differentiable operation during the forward pass, but approximate it with a differentiable proxy during the backward pass.
Revised: The basic idea is to perform non-differentiable operation during the forward pass, but approximate it with a differentiable proxy during the backward pass.

Revised: We borrow the notation from Jang et al.

Revised: for the following introduction.

Original: Consider sampling from a categorical distribution, the following operation is performed during the forward pass
Revised: Consider sampling from a categorical distribution, the following operation is performed during the forward pass

Original: [Equation]
Revised: [Equation]

Original: where  categorical samples are encoded as  v -dimensional one-hot vectors ( v being the given vocabulary size).
Revised: where a categorical sample ^ x is encoded as a v -dimensional one-hot vector z ( v being the given vocabulary size).

Original: During the backward pass, the corresponding straight-through estimator of function f with respect to (cid:18) is
Revised: During the backward pass, the corresponding straight-through estimator of function f with respect to (cid:18) is

Original: [Equation]
Revised: [Equation]

Original: derived by using the approximation @
Revised: derived by using the approximation @

Revised: z

Revised: [Equation]

Revised: (cid:18) (cid:25)

Original: z @(cid:18) (cid:25) @ m (cid:18) @(cid:18) , where m (cid:18) is a differentiable proxy for the non-differentiable one-hot encoding vector z
Revised: [Equation] m [Equation] @(cid:18) , where m (cid:18) is a differentiable proxy for the non-differentiable one-hot encoding vector z

Original: (Jang et al., 2016).
Revised: (Jang et al., 2016).

Original: Moreover, Jang et al.
Revised: Moreover, Jang et al.

Original: (2016) suggest the choice of
Revised: (2016) suggest the choice of

Original: [Equation]
Revised: [Equation]

Original: ) as proxy, where  x is a possible token and V is the number of all possible tokens.
Revised: ) as proxy, where f x 1 ; : : : ; x v g are all possible tokens in vocabulary V .

Original: Compared to the former method, this approach updates all the possible tokens’ probability, even if the tokens were not sampled.
Revised: Compared to the former method, this approach updates all the possible tokens’ probability, even if the tokens were not sampled.

Original:  Proposed Method
Revised:  Proposed Method

Original: The non-differentiable connection between the generator G (cid:18) and the discriminator D ϕ is our primary focus in this paper (Figure 1).
Revised: The non-differentiable connection between the generator G (cid:18) and the discriminator D ϕ is our primary focus in this paper (Figure 1).

Original: Starting from the straight through gradient estimator, we derive a re-centered estimator which helps the generator learn to select better tokens.
Revised: Starting from the straight through gradient estimator, we derive a re-centered estimator which helps the generator learn to select better tokens.

Original: 3.1 Gradient Estimation in the Generator
Revised: 3.1 Gradient Estimation in the Generator

Original: Consider a discriminator whose first layer is an embedding lookup layer.
Revised: Consider a discriminator whose first layer is an embedding lookup layer.

Original: During forward propagation, the operation below is performed first:
Revised: During forward propagation, the operation below is performed first:

Original: [Equation]
Revised: [Equation]

Original: [Equation]

Original: where E = [ e 1 ; : : : ; e v ] and z is one-hot encoding of vocabulary.
Revised: where E = [ e 1 ; : : : ; e v ] and z is one-hot encoding of vocabulary.

Original: As described in Section 2.2, this is a non-differentiable categorical sampling operation with a straight-through estimator of the gradient of V G with respect to (cid:18) , formulated as follows:
Revised: As described in Section 2.3, this is a non-differentiable categorical sampling operation with a straight-through estimator of the gradient of V G with respect to (cid:18) , formulated as follows:

Original: [Equation]
Revised: [Equation]

Original: 9) where ^ x t is the selected token at time-step t , T is the maximum time-step and V is the given vocabulary.
Revised: where ^ x t is the selected token at time-step t , T is the maximum time-step and V is the given vocabulary.

Original: The first term of Equation 8 can be derived from Equation 7 by applying the chain rule.
Revised: The first term of Equation 8 can be derived from Equation 7 by applying the chain rule.

Original: 3.2 Re-centered Estimator
Revised: 3.2 Re-centered Estimator

Original: We turn our attention to the first term of Equation 9:
Revised: We turn our attention to the first term of Equation 9:

Original: [Equation]
Revised: [Equation]

Original: Denoting it as (cid:14)  x t  [ V G ] , Equation 9 becomes
Revised: Denoting it as (cid:14) ^ x t ! x [ V G ] , Equation 9 becomes

Original: [Equation]
Revised: [Equation]

Original: The coefficient (cid:14)  x t  [ V G ] can be considered as an affinity factor indicating how much the discriminator is ‘attracted’ to the token xt .
Revised: The coefficient (cid:14) ^ x t ! x [ V G ] can be considered as an affinity factor indicating how much the discriminator is ‘attracted’ to the token x .

Original: Recall that the gradient @
Revised: Recall that the gradient @

Original: V G @
Revised: V G @

Original: e ( e ^ x ) has the direction of greatest increase of the objective V G at e ^ x .
Revised: e ( e ^ x ) has the direction of greatest increase of the objective V G at e ^ x .

Original: Since this direction is relative to e ^ x , we suggest taking the inner product with the vector e x (cid:0) e ^ x which has been re-centered relative to e ^ x (Figure 2). 
Revised: Since this direction is relative to e ^ x , we suggest taking the inner product with the vector e x (cid:0) e ^ x which has been re-centered relative to e ^ x (Figure 2). [Equation]

Original: [Equation]
Revised: [Equation]

Original: This inner product approximates the increase of V G when ^ x t is replaced by xt .
Revised: This inner product approximates the increase of V G when ^ x t is replaced by x .

Original: Because this approximation only holds in the neighborhood of e ^ x t , j e xt (cid:0) e ^ x t j ≪ 1 , we also add a kernel to dampen the affinity factor for e t that are far away.
Revised: Because this approximation only holds in the neighborhood of e ^ x t , j e x (cid:0) e ^ x t j ≪ 1 , we also add a kernel to dampen the affinity factor for e x that are far away.

Original: That is,
Revised: That is,

Original: [Equation]

Original: [Equation]
Revised: [Equation]

Original:  where
Revised: 13) where

Original: [Equation]
Revised: [Equation]

Original: See Appendix A for more details.
Revised: See Appendix B for more details.

Original: As a result, Equation 9 becomes
Revised: As a result, Equation 9 becomes

Original: [Equation]
Revised: [Equation]

Original: In score function-based approaches, the generator is only given feedback on the tokens it samples, so it requires a large amount of trial-and-error in order to figure out how to write realistic sentences.
Revised: In score function-based approaches, the generator is only given feedback on the tokens it samples, so it requires a large amount of trial-and-error in order to figure out how to write realistic sentences.

Original: To use an analogy, imagine a captain at sea (embedding space) searching for treasure.
Revised: To use an analogy, imagine a captain at sea (embedding space) searching for treasure.

Original: Each iteration of the game, the captain chooses random locations in the space and receives feedback regarding the choices.
Revised: Each iteration of the game, the captain chooses random locations in the space and receives feedback regarding the choices.

Original: Score function-based approaches tell us how good the location is but say nothing about how to improve it.
Revised: Score function-based approaches tell us how good the location is but say nothing about how to improve it.

Original: In contrast, our method, gives the captain a navigational direction to the next best location.
Revised: In contrast, our method, gives the captain a navigational direction to the next best location.

Original: Even if the direction is biased, it should allow the captain to find the treasures with fewer iterations.
Revised: Even if the direction is biased, it should allow the captain to find the treasures with fewer iterations.

Original:  Evaluation Metrics
Revised:  Evaluation Metrics

Original: We evaluate our model’s ability to generate realistic and diverse texts with BLEU-based metric,
Revised: We evaluate our model’s ability to generate realistic and diverse texts with BLEU-based metric,

Original: Fréchet Embedding Distance and language model based metric.
Revised: Fréchet Embedding Distance and language model based metric.

Original: 4.1 BLEU and Self-BLEU
Revised: 4.1 BLEU and Self-BLEU

Original: BLEU (Papineni et al., 2002) and Self-BLEU (Zhu et al., 2018) capture text quality and diversity respectively.
Revised: BLEU (Papineni et al., 2002) and Self-BLEU (Zhu et al., 2018) capture text quality and diversity respectively.

Original: BLEU, a modified form of n-gram precision, measures local consistency between a set of reference and a set of candidate texts.
Revised: BLEU, a modified form of n-gram precision, measures local consistency between a set of reference and a set of candidate texts.

Original: Self-BLEU is a version of BLEU in which both the reference and candidate texts are drawn from the same corpus.
Revised: Self-BLEU is a version of BLEU in which both the reference and candidate texts are drawn from the same corpus.

Original: A high
Revised: A high

Original: Self-BLEU means that members within the corpus are too highly correlated, indicating a lack of diversity.
Revised: Self-BLEU means that members within the corpus are too highly correlated, indicating a lack of diversity.

Revised: [Figure]

Revised: (a) The inner products of A and B with ∇ e V G are both positive even though B leads to a lower V G .

Revised: (b) After applying re-centering in the inner product, the affinity factors (cid:14) RC (cid:3)!

Original:  4.2 Fréchet Embedding Distance
Revised: B [ V G ] , [Figure] A [ V G ] have opposite signs, and thus better at reflecting the relative suitability of A and B as judged by the discriminator 4.2 Fréchet Embedding Distance

Original: Fréchet Embedding Distance (FED) (de Masson d’Autume et al., 2019) measures sentimental similarity, global consistency and diversity between reference and candidate texts.
Revised: Fréchet Embedding Distance (FED) (de Masson d’Autume et al., 2019) measures sentimental similarity, global consistency and diversity between reference and candidate texts.

Original: Semeniuta et al.
Revised: Semeniuta et al.

Original: (2018) claims that Fréchet Distance (FD) is not sensitive to the choice of embedding model.
Revised: (2018) claims that Fréchet Distance (FD) is not sensitive to the choice of embedding model.

Original: However, we notice significant discrepancies between FD scores calculated using different embedding models.
Revised: However, we notice significant discrepancies between FD scores calculated using different embedding models.

Original: The details are discussed in Appendix F.
Revised: The details are discussed in Appendix G.

Original: [Figure]

Original: (a) The inner products of A and B with ∇ e V G are both positive even though B leads to a lower V G .

Original: (b) After applying re-centering in the inner product, the affinity factors (cid:14) RCB

Original: [ V G ] , (cid:14) RCA [ V G ] have opposite signs, and thus better at reflecting the relative suitability of A and B as judged by the discriminator

Original: 4.3 Language Model Scoring Methods
Revised: 4.3 Language Model Scoring Methods

Original: Following Caccia et al.
Revised: Following Caccia et al.

Original: ; Zhao et al.
Revised: ; Zhao et al.

Original: (2017), we also evaluate the quality and the diversity of generated samples by Language Model Perplexity (LM) and Reverse Language
Revised: (2017), we also evaluate the quality and the diversity of generated samples by Language Model Perplexity (LM) and Reverse Language

Original: Model Perplexity (RLM).
Revised: Model Perplexity (RLM).

Original: Language Model Score (LM) .
Revised: Language Model Score (LM) .

Original: We use a language model trained on real data to estimate the negative log likelihood per word of generated sentences.
Revised: We use a language model trained on real data to estimate the negative log likelihood per word of generated sentences.

Original: Samples which the language model considers improper, such as ungrammatical or nonsense sentences, have high perplexity.
Revised: Samples which the language model considers improper, such as ungrammatical or nonsense sentences, have high perplexity.

Original: Reverse Language Model Score (RLM) .
Revised: Reverse Language Model Score (RLM) .

Original: If we instead train a language model on generated data while evaluating on real data, we can judge the diversity of the generated samples.
Revised: If we instead train a language model on generated data while evaluating on real data, we can judge the diversity of the generated samples.

Original: Low-diversity generated data leads to an overfitted model which generalizes poorly on real data, as indicated by a high perplexity.
Revised: Low-diversity generated data leads to an overfitted model which generalizes poorly on real data, as indicated by a high perplexity.

Original:  Results & Discussion
Revised:  Results & Discussion

Original: In this section, we use two datasets to evaluate CaptainGAN: COCO Image Captions 1 and
Revised: In this section, we use two datasets to evaluate CaptainGAN: COCO Image Captions 1 and

Original: EMNLP 2017 News 2 .
Revised: EMNLP 2017 News 2 .

Original: We discuss our results from different perspectives via the evaluation metrics presented in the last section.
Revised: We discuss our results from different perspectives via the evaluation metrics presented in the last section.

Original: Furthermore, an ablation study is performed in order to know the contribution of each technique.
Revised: Furthermore, an ablation study is performed in order to know the contribution of each technique.

Original: More details about our experiment setup, datasets, architecture and training techniques such as temperature control are described in
Revised: More details about our experiment setup, datasets, architecture and training techniques such as temperature control are described in

Original: Appendix C.
Revised: Appendix D.

Revised: [Figure]

Revised: [Table]

Original: 5.1 Quality and Diversity
Revised: 5.1 Quality and Diversity

Original: CaptainGAN significantly outperforms prior GAN-based models on metrics of local text consistency and diversity.
Revised: CaptainGAN significantly outperforms prior GAN-based models on metrics of local text consistency and diversity.

Original: The BLEU/Self-BLEU temperature sweeps shown in Figure 3a for CaptainGAN indicates that CaptainGAN approaches the performance of a language model.
Revised: The BLEU/Self-BLEU temperature sweeps shown in Figure 3a for CaptainGAN indicates that CaptainGAN approaches the performance of a language model.

Original: On the other hand, in Figure 3b, the language model scores show similar results for
Revised: On the other hand, in Figure 3b, the language model scores show similar results for

Original: CaptainGAN and MLE model.
Revised: CaptainGAN and MLE model.

Original: Results for COCO are shown in Appendix D.
Revised: Results for COCO are shown in Appendix E.

Original: [Figure]

Original: [Table]

Original: 5.2 Temperature Sensitivity
Revised: 5.2 Temperature Sensitivity

Original: CaptainGAN is robust to temperature variations.
Revised: CaptainGAN is robust to temperature variations.

Original: This is demonstrated in Figure 4a [Table] which depicts how the softmax temperature influences the FED score.
Revised: This is demonstrated in Figure 4a, which depicts how the softmax temperature influences the FED score.

Original: Regardless of model, changes in softmax temperature are certain to affect the FED, however, CaptainGAN shows the least variation.
Revised: Regardless of model, changes in softmax temperature are certain to affect the FED, however, CaptainGAN shows the least variation.

Original: 5.3 Global Consistency
Revised: 5.3 Global Consistency

Original: CaptainGAN improves global consistency comparing to prior works and language model on
Revised: CaptainGAN improves global consistency comparing to prior works and language model on

Original: LM perplexity and FED score, respectively.
Revised: LM perplexity and FED score, respectively.

Original: As shown in Table 1, our method significantly reduces the gap of perplexity between GAN-based method and language model, which is  directly trained to minimize the perplexity.
Revised: As shown in Table 1, our method significantly reduces the gap of perplexity between GAN-based method and language model, which is [Figure] directly trained to minimize the perplexity.

Original: Besides, we show that CaptainGAN outperforms
Revised: Besides, we show that CaptainGAN outperforms

Original: MLE trained model on FED score as shown in Table [Table]
Revised: MLE trained model on FED score as shown in Table 1.

Original: 5.4 Matching N-grams of Training Data
Revised: 5.4 Matching N-grams of Training Data

Original: Text generating models are often criticized for simply memorizing texts seen during training instead of learning how to model the true data distribution.
Revised: Text generating models are often criticized for simply memorizing texts seen during training instead of learning how to model the true data distribution.

Original: We show the longest matching ngrams between text from the training data and samples in Figure 4b.
Revised: We show the longest matching ngrams between text from the training data and samples in Figure 4b.

Original: CaptainGAN generates more samples with small matching n-grams (n < 5) comparing to MLE model, implying[Figure] [Table] that our model can generate novel sentences without resorting to memorizing phrases from the training data.
Revised: CaptainGAN generates more samples with small matching n-grams (n < 5) comparing to MLE model, implying that our model can generate novel sentences without resorting to memorizing phrases from the training data.

Original: 5.5 CaptainGan As A Language Model
Revised: 5.5 CaptainGan As A Language Model

Original: CaptainGAN’s strong performance on the evaluation metrics described earlier shows that it is capable of generating realistic text.
Revised: CaptainGAN’s strong performance on the evaluation metrics described earlier shows that it is capable of generating realistic text.

Original: Since the generator in our case is a RNN which can produce conditional probabilities at each timestep, this raises the question of whether or not an explicit language model is being learned.
Revised: Since the generator in our case is a RNN which can produce conditional probabilities at each timestep, this raises the question of whether or not an explicit language model is being learned.

Original: We feed real data to our generator and calculate the perplexity to measure the generator’s suitability as a language model.
Revised: We feed real data to our generator and calculate the perplexity to measure the generator’s suitability as a language model.

Original: Surprisingly, as shown in Table 2, the perplexity of the generator on both training and validation data is unusually high.
Revised: Surprisingly, as shown in Table 2, the perplexity of the generator on both training and validation data is unusually high.

Original: A similar phenomenon was noted in de Masson d’Autume et al.
Revised: A similar phenomenon was noted in de Masson d’Autume et al.

Original: (2019), though not as extreme.
Revised: (2019), though not as extreme.

Original: We explain the phenomenon by the following reasons:
Revised: We explain the phenomenon by the following reasons.

Original: • MLE model is trained specifically for low perplexity, unlike CaptainGAN which would be penalized by the discriminator if the generator favors quality over diversity. • Perplexity is very sensitive to names, places, punctuation marks, or other contextspecific words that are not necessary for coherent and grammatically correct text. Poor modeling of the likelihood is not indicative of poor sample generation. (Theis et al., 2016; Hashimoto et al., 2019).
Revised: Minimizing alternative loss (Equation 2) is similar to minimizing reverse KL divergence (Arjovsky & Bottou, 2017).

Original: • Our discriminator is convolution-based which means it is not as sensitive to the specific word as recurrent-based model.
Revised: This objective assigns an extremely low cost on mode dropping and does not force the generator to mimic every part of real data.

Original: Following 5.4, we conclude that our model’s ability to generate realistic text cannot be the result of simply plagiarizing training samples.
Revised: Following 5.4, we conclude that our model’s ability to generate realistic text cannot be the result of simply plagiarizing training samples.

Original: [Table]

Original: 5.6 Comparison to score function-based methods
Revised: 5.6 Comparison to score function-based methods

Original: Our method is able to outperform score function-based approaches using small batch sizes.
Revised: Our method is able to outperform score function-based approaches using small batch sizes.

Original: Score function-based approaches often rely on large batch sizes for variance reduction (de Masson d’Autume et al., 2019), but this does not appear to be necessary for our recentered estimator.
Revised: Score function-based approaches often rely on large batch sizes for variance reduction (de Masson d’Autume et al., 2019), but this does not appear to be necessary for our recentered estimator.

Original: This confirms the reasoning at the end of Section 3.2, and greatly reduces our memory requirements during training. 
Revised: This confirms the reasoning at the end of Section 3.2, and greatly reduces our memory requirements during training. [Table] [Table]

Original: In addition, since gradients are naturally calculated for every timestep, there is no need for
Revised: In addition, since gradients are naturally calculated for every timestep, there is no need for

Original: Monte-Carlo estimation of rewards like in score function-based approaches.
Revised: Monte-Carlo estimation of rewards like in score function-based approaches.

Original: Because our generator receives a richer learning signal, we use a 5:1 ratio for discriminator-generator updating.
Revised: Because our generator receives a richer learning signal, we use a 5:1 ratio for discriminator-generator updating.

Original: This greatly lessens our computational requirements.
Revised: This greatly lessens our computational requirements.

Original: 5.7 Ablation Study
Revised: 5.7 Ablation Study

Original: To understand the impact of each component of the CaptainGAN, we conduct an ablation study.
Revised: To understand the impact of each component of the CaptainGAN, we conduct an ablation study.

Original: In Table 3, we show the influence of adding new features, namely spectral normalization, re-center estimator, pretrained embeddings and discriminator regularization decoupled weight decay (Loshchilov & Hutter, 2018) and one-sided label smoothing (Salimans et al., 2016), by scoring FED on validation data.
Revised: In Table 3, we show the influence of adding new features, namely spectral normalization, re-center estimator, pretrained embeddings and discriminator regularization decoupled weight decay (Loshchilov & Hutter, 2018) and one-sided label smoothing (Salimans et al., 2016), by scoring FED on validation data.

Original: We observe that:
Revised: We observe that:

Original: • As noted earlier, spectral normalization is critical for our method as without it the model cannot converge.
Revised: • As noted earlier, spectral normalization is critical for our method as without it the model cannot converge.

Original: • Rather unexpectedly, pretrained embeddings do not lead to any significant improvement, which is surprising given our method’s dependence on the discriminator’s embedding space.
Revised: • Rather unexpectedly, pretrained embeddings do not lead to any significant improvement, which is surprising given our method’s dependence on the discriminator’s embedding space.

Original: The embeddings which the discriminator learns from scratch are capable of giving the generator an effective learning signal.
Revised: The embeddings which the discriminator learns from scratch are capable of giving the generator an effective learning signal.

Original: • The application of the re-centered estimator is responsible for a 20% improvement in FED, confirming its effectiveness.
Revised: • The application of the re-centered estimator is responsible for a 20% improvement in FED, confirming its effectiveness.

Original: • Equation 14 is dependent on the norm of the embeddings.
Revised: • Equation 14 is dependent on the norm of the embeddings.

Original: This dependency explains the effectiveness of discriminator weight regularization at improving FED score.
Revised: This dependency explains the effectiveness of discriminator weight regularization at improving FED score.

Original:  Conclusion and Future Work
Revised:  Conclusion and Future Work

Original: In this work we have presented CaptainGAN, an effective gradient-based method for quickly training a text generating GAN without pretraining.
Revised: In this work we have presented CaptainGAN, an effective gradient-based method for quickly training a text generating GAN without pretraining.

Original: Starting from the straight-through estimator, we derive a re-centered gradient estimator that improves the quality/diversity of generated texts as measured by various standard metrics.
Revised: Starting from the straight-through estimator, we derive a re-centered gradient estimator that improves the quality/diversity of generated texts as measured by various standard metrics.

Original: In future work, we plan to investigate our estimator in more theoretical detail and further decrease its bias.
Revised: In future work, we plan to investigate our estimator in more theoretical detail and further decrease its bias.

Original: Appendix A  The Importance of Kernel
Revised: Appendix A Notation Appendix B The Importance of Kernel

Original: To verify the necessity of the kernel term (Equation 14), we directly apply Equation 12 to
Revised: To verify the necessity of the kernel term (Equation 14), we directly apply Equation 12 to

Original: Equation 11 as follows:
Revised: Equation 11 as follows:

Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: 16) where ^ x is the selected token and x is a tokens in the vocabulary V.
Revised: where ^ x is the selected token and x is a tokens in the vocabulary V.

Original: Let f ( x ) = ⟨ e x ; @ V G @
Revised: Let f ( x ) = ⟨ e x ; @ V G @

Original: e ( e ^ x ) ⟩ and b = ⟨ e ^ x ; @ V G @
Revised: e ( e ^ x ) ⟩ and b = ⟨ e ^ x ; @ V G @

Original: e ( e ^ x ) ⟩ .
Revised: e ( e ^ x ) ⟩ .

Original: b does not depend on x and is constant in the summation. 
Revised: b does not depend on x and is constant in the summation. ∑

Revised: [Equation]

Revised: ( f ( x )

Original: [Equation]
Revised: [Equation]

Revised: b )

Original: [Equation]
Revised: [Equation]

Revised: p (cid:18) ( x ) @(cid:18) = @ @(cid:18)

Revised: [ ∑ x 2 V ( f ( x ) + b ) p (cid:18) ( x )]

Revised: = @

Revised: @(cid:18)

Revised: [( ∑ x 2 V f ( x ) p (cid:18) ( x ))

Revised: + b ( ∑ x 2 V p (cid:18) ( x ))]

Revised: = @

Revised: @(cid:18)

Revised: [( ∑ x 2 V f ( x ) p (cid:18) ( x ))

Revised: + b (cid:3) 1] = @

Revised: @(cid:18)

Revised: ∑ x 2 V f ( x ) p (cid:18) ( x ) (17)

Original: Without the kernel, the update to (cid:18) will be the same as prior work and thus carries the same drawback.
Revised: Without the kernel, the update to (cid:18) will be the same as prior work and thus carries the same drawback.

Original: Appendix B Model Structure
Revised: Appendix C Model Structure

Original: We use a pre-trained word embedding of 300 dimensions to initialize all embedding weights.
Revised: We use a pre-trained word embedding of 300 dimensions to initialize all embedding weights.

Original: The embeddings are pre-trained using the fastText library Bojanowski et al.
Revised: The embeddings are pre-trained using the fastText library Bojanowski et al.

Original: (2016) on the corresponding training data of each task.
Revised: (2016) on the corresponding training data of each task.

Original: [Table] 
Revised: [Table] C.1 Generator

Original: The purpose of the first dense layer is to project the GRU cell outputs into the embedding space.
Revised: The purpose of the first dense layer is to project the GRU cell outputs into the embedding space.

Original: The second dense layer converts the projected embedding vectors to logits over the tokens and is initialized as the transpose of the embedding weights.
Revised: The second dense layer converts the projected embedding vectors to logits over the tokens and is initialized as the transpose of the embedding weights.

Original: At the first timestep, the input to the embedding layer is the start-of-sentence token.
Revised: At the first timestep, the input to the embedding layer is the start-of-sentence token.

Original: The vocabulary also contains an end-of-sentence token.
Revised: The vocabulary also contains an end-of-sentence token.

Original: If the generator outputs this token at any timestep, the sentence ends and its length is set to this timestep.
Revised: If the generator outputs this token at any timestep, the sentence ends and its length is set to this timestep.

Original: B.2 Discriminatorembedding lookup
Revised: C.2 Discriminator

Original: [Table]
Revised: [Table]

Original: To perform consistent convolution on sentences of different lengths, we use a special masking mechanism on all layers.
Revised: To perform consistent convolution on sentences of different lengths, we use a special masking mechanism on all layers.

Original: For all convolution and pooling layers, we mask out input features where the receptive field is out-of-bounds with respect to the unpadded input sentence.
Revised: For all convolution and pooling layers, we mask out input features where the receptive field is out-of-bounds with respect to the unpadded input sentence.

Original: Appendix C Experimental details C.1 Setup C.1.1 Datasets
Revised: Appendix D Experimental details D.1 Setup D.1.1 Datasets

Original: We use two datasets COCO Image Caption dataset Chen et al.
Revised: We use two datasets COCO Image Caption dataset Chen et al.

Original: and EMNLP
Revised: and EMNLP

Original: News dataset 3 .
Revised: News dataset 3 .

Original: Results are reported on EMNLP 2017 News if not specified.
Revised: Results are reported on EMNLP 2017 News if not specified.

Original: For each datasets, 10k sentences are set aside as validation data.
Revised: For each datasets, 10k sentences are set aside as validation data.

Original: COCO Image Captions.
Revised: COCO Image Captions.

Original: Sentences are limited in length to 24 tokens.
Revised: Sentences are limited in length to 24 tokens.

Original: The vocabulary size is 4.6k tokens.
Revised: The vocabulary size is 4.6k tokens.

Original: Training data consists of 10k sentences 4 .
Revised: Training data consists of 10k sentences 4 .

Original: COCO

Original: EMNLP 2017 News.
Revised: EMNLP 2017 News.

Original: Sentences in this dataset are much longer and more complicated, with a maximum length of 50 tokens and a vocabulary of 5.7k words.
Revised: Sentences in this dataset are much longer and more complicated, with a maximum length of 50 tokens and a vocabulary of 5.7k words.

Original: Training data consists of 300k sentences.
Revised: Training data consists of 300k sentences.

Original: C.1.2 Architecture and Techniques
Revised: D.1.2 Architecture and Techniques

Original: Our architecture is described in Appendix B. Spectral normalization of the discriminator
Revised: Our architecture is described in Appendix C. Spectral normalization of the discriminator

Original: ’s weights Miyato et al.
Revised: ’s weights Miyato et al.

Original: (2018) is critical to our method as without it the gradients are too unstable and convergence becomes impossible (Section 5.7).
Revised: (2018) is critical to our method as without it the gradients are too unstable and convergence becomes impossible (Section 5.7).

Original: C.1.3 Temperature Control
Revised: D.1.3 Temperature Control

Original: Following Caccia et al.
Revised: Following Caccia et al.

Original: (2018), we adjust the softmax temperature parameter at sampling time to measure the trade-off between quality and diversity.
Revised: (2018), we adjust the softmax temperature parameter at sampling time to measure the trade-off between quality and diversity.

Original: Increasing the temperature lowers the differences between softmax probabilities, which leads to diverse but low-quality samples.
Revised: Increasing the temperature lowers the differences between softmax probabilities, which leads to diverse but low-quality samples.

Original: On the other hand, reducing the temperature leads to high-quality yet low-diversity samples.
Revised: On the other hand, reducing the temperature leads to high-quality yet low-diversity samples.

Original: C.2 training details
Revised: D.2 training details

Original: The discriminator and generator updates are performed with a 5:1 ratio.
Revised: The discriminator and generator updates are performed with a 5:1 ratio.

Original: We use Adam
Revised: We use Adam

Original: Kingma & Ba (2014) with learning rate = 5 : 0 (cid:1) 10 (cid:0) 5 , (cid:12) 1 = 0 : 5 , and (cid:12) 2 = 0 : 999 .
Revised: Kingma & Ba (2014) with learning rate = 5 : 0 (cid:1) 10 (cid:0) 5 , (cid:12) 1 = 0 : 5 , and (cid:12) 2 = 0 : 999 .

Original: Decoupled weight decay regularization Loshchilov & Hutter (2018) is applied to all variables in the discriminator, and (cid:21) = 0 : 05 (cid:1) learning rate.
Revised: Decoupled weight decay regularization Loshchilov & Hutter (2018) is applied to all variables in the discriminator, and (cid:21) = 0 : 05 (cid:1) learning rate.

Original: All our models are trained using an NVIDIA
Revised: All our models are trained using an NVIDIA

Original: GTX 1080 Ti.
Revised: GTX 1080 Ti.

Original: To evaluate samples on BLEU-5, LM versus RLM, and FED, we use 10k validation sentences as reference texts for both COCO and EMNLP 2017 News.
Revised: To evaluate samples on BLEU-5, LM versus RLM, and FED, we use 10k validation sentences as reference texts for both COCO and EMNLP 2017 News.

Original: For Self-BLEU, we randomly select half of the 10k samples as reference texts and leave the remainder as target texts.
Revised: For Self-BLEU, we randomly select half of the 10k samples as reference texts and leave the remainder as target texts.

Original: A smoothing function is used on BLEU-based metrics.
Revised: A smoothing function is used on BLEU-based metrics.

Original: See Appendix C.4 for more details.
Revised: See Appendix D.4 for more details.

Original: The maximum likelihood model architecture in this paper follows the setting of de Masson d’Autume et al.
Revised: The maximum likelihood model architecture in this paper follows the setting of de Masson d’Autume et al.

Original: We could not reproduce the exact results reported in de Masson d’Autume et al.
Revised: We could not reproduce the exact results reported in de Masson d’Autume et al.

Original: (2019), which we believe stems from discrepancies between our trained language model and theirs.
Revised: (2019), which we believe stems from discrepancies between our trained language model and theirs.

Original: C.3 One-sided label smoothing
Revised: D.3 One-sided label smoothing

Original: One-sided label smoothing has been shown to reduce the vulnerability of neural networks to adversarial examples and it is strongly recommended for GAN training Salimans et al.
Revised: One-sided label smoothing has been shown to reduce the vulnerability of neural networks to adversarial examples and it is strongly recommended for GAN training Salimans et al.

Original: Therefore, the first term of Equation 1 is reformulated as
Revised: Therefore, the first term of Equation 1 is reformulated as

Original: [Equation]
Revised: [Equation]

Original: where (cid:11) = 0 : 9 in our experiments.
Revised: where (cid:11) = 0 : 9 in our experiments.

Original: C.4 BLEU smoothing
Revised: D.4 BLEU smoothing

Original: One of the issue with BLEU is that in the case that a higher order n-gram precision of a sentence is 0, then the BLEU score will be 0, resulting in severely underestimation.
Revised: One of the issue with BLEU is that in the case that a higher order n-gram precision of a sentence is 0, then the BLEU score will be 0, resulting in severely underestimation.

Original: This is due to the fact that BLEU is calculated by the geometric mean of precision.
Revised: This is due to the fact that BLEU is calculated by the geometric mean of precision.

Original: To solve this, we applied the smoothing technique as follows:
Revised: To solve this, we applied the smoothing technique as follows:

Original: [Equation]
Revised: [Equation]

Original: (19) where m n is the original match count, m ′ n is the one used during BLEU calculation.
Revised: 19) where m n is the original match count, m ′ n is the one used during BLEU calculation.

Original: We’ve picked the smoothing factor ϵ = 0 : 1 as proposed by Chen & Cherry (2014).[Figure]
Revised: We’ve picked the smoothing factor ϵ = 0 : 1 as proposed by Chen & Cherry (2014).

Original: Appendix D COCO Results
Revised: Appendix E COCO Results

Revised: [Figure]

Original: See Figure 6.
Revised: See Figure 6.

Original: Appendix E Sentence Length Consistency
Revised: Appendix F Sentence Length Consistency

Original: The sentence length of samples which are generated by CaptainGAN is highly related to the training data, as shown in Figure 7.
Revised: The sentence length of samples which are generated by CaptainGAN is highly related to the training data, as shown in Figure 7.

Original: This suggests that CaptainGAN is able to fully learn the distribution of sentence length by gradient estimator without additional mechanism like positional encoding or dense reward.
Revised: This suggests that CaptainGAN is able to fully learn the distribution of sentence length by gradient estimator without additional mechanism like positional encoding or dense reward.

Original: Appendix F Details on Fréchet Embedding Distance
Revised: Appendix G Details on Fréchet Embedding Distance

Original: Calculation of the FED score depends on the Universal Sentence EncoderCer et al.
Revised: Calculation of the FED score depends on the Universal Sentence EncoderCer et al.

Original: However, the version of Universal Sentence Encoder 6 used in de Masson d’Autume et al.
Revised: However, the version of Universal Sentence Encoder 6 used in de Masson d’Autume et al.

Original: (2019) is incompatible with TensorflowAbadi et al.
Revised: (2019) is incompatible with TensorflowAbadi et al.

Original: (2015) version 1.7 or newer.
Revised: (2015) version 1.7 or newer.

Original: Furthermore, it would be infeasible to limit the experimentation environment to such ancient version of
Revised: Furthermore, it would be infeasible to limit the experimentation environment to such ancient version of

Original: [Table]

Original: Tensorflow.
Revised: Tensorflow.

Original: Therefore, as a work around, we decide to report our FED score, as shown in Table 6, with Universal Sentence Encoder
Revised: Therefore, as a work around, we decide to report our FED score, as shown in Table 7, with Universal Sentence Encoder

Original: Large 7 instead, which is compatible with the current version of Tensorflow (1.14 as of Jul 2019).
Revised: Large 7 instead, which is compatible with the current version of Tensorflow (1.14 as of Jul 2019).

Original: While there are many benefits to using the FED metric, it is not without drawback.
Revised: While there are many benefits to using the FED metric, it is not without drawback.

Original: A drawback that we’ve observed is that the FED score will be significantly underestimated  whenever we are using a small number of samples, as shown in Figure 8.
Revised: A drawback that we’ve observed is that the FED score will be significantly underestimated [Table] whenever we are using a small number of samples, as shown in Figure 8.

Original: Therefore, we’ve ensured that sufficient samples are used in our evaluation.
Revised: Therefore, we’ve ensured that sufficient samples are used in our evaluation

Revised: [Table]

Original: Appendix G Generated samples
Revised: Appendix H Generated samples

Original: Training samples of COCO and EMNLP 2017 News can be found in Table 7.
Revised: Training samples of COCO and EMNLP 2017 News can be found in Table 8.

Original: Randomly picked samples from MLE model and CaptainGAN are available in Table 8 and Table 9.
Revised: Randomly picked samples from MLE model and CaptainGAN are available in Table 9 and Table [Table]

Original: COCO - a person standing in a boat on the water .
Revised: - a person standing in a boat on the water .

Original: - three sheep are grazing on the city sidewalk .
Revised: - three sheep are grazing on the city sidewalk .

Original: - kenya airways airplane wing , engine and cabin on the tarmac . - light poles in the snow with yellow traffic lights mounted on them .
Revised: - kenya airways airplane wing , engine and cabin on the tarmac . - light poles in the snow with yellow traffic lights mounted on them .

Original: - we are looking at the floor between the toilet and the wall . -
Revised: - we are looking at the floor between the toilet and the wall . -

Original: someone taking a photo of a small residential bathroom .
Revised: someone taking a photo of a small residential bathroom .

Original: - a cat sits on the seat of a bicycle and looks down at another cat on a snowy day .
Revised: - a cat sits on the seat of a bicycle and looks down at another cat on a snowy day .

Original: - large set of motorcycles all lined up down a street .
Revised: - large set of motorcycles all lined up down a street .

Original: EMNLP 2017 News
Revised: EMNLP 2017 News

Original: - apple clearly doesn ’ t want to see the dollar value of its u .
Revised: - apple clearly doesn ’ t want to see the dollar value of its u .

Original: k .
Revised: k .

Original: earnings fall by a similar amount .
Revised: earnings fall by a similar amount .

Original: - we do look at the number of hours we produce , and measure that against the religious make - up of society . - the u .
Revised: - we do look at the number of hours we produce , and measure that against the religious make - up of society . - the u .

Original: s . has struggled to find an effective ground force to take on isis in syria , where president obama has ruled out a u .
Revised: s . has struggled to find an effective ground force to take on isis in syria , where president obama has ruled out a u .

Original: s .
Revised: s .

Original: ground combat role .
Revised: ground combat role .

Original: - it is a happy and kind world that we live in on this show and that is where i hope we can live in real life . - while men and women may end up earning roughly the same amount in the same jobs , men are more likely to end up in higher - paying roles in the tech industry .
Revised: - it is a happy and kind world that we live in on this show and that is where i hope we can live in real life . - while men and women may end up earning roughly the same amount in the same jobs , men are more likely to end up in higher - paying roles in the tech industry .

Original: - but while they were beaten by a better side , the tie did reveal what i think has been city ’ s biggest problem this season: they have lost the ability to score against good teams .
Revised: - but while they were beaten by a better side , the tie did reveal what i think has been city ’ s biggest problem this season

Revised: : they have lost the ability to

Revised: score

Original:  - according to facebook ’ s policies , accounts can be suspended if law enforcement believe individuals are at risk of harm . 
Revised: against good teams . - according to facebook ’ s policies , accounts can be suspended if law enforcement believe individuals are at risk of harm . - she

Revised: has to take personal

Revised: responsibility

Revised: for

Revised: this

Original: - she has to take personal responsibility for this - when she was health secretary she was told bythose who know best that her decision to cut student places would have a damaging impact .
Revised: when she was health secretary she was told by

Revised: those who

Revised: know best that her

Revised: decision

Revised: to cut student places

Revised: would

Revised: have

Revised: a damaging impact

Original: COCO
Revised: COCO

Original: - the bathroom bowl with focus on the vanity
Revised: - the bathroom bowl with focus on the vanity

Original: - a man racer in a car brushing a carved cake . -
Revised: - a man racer in a car brushing a carved cake . -

Original: a lady riding a motorcycle and a street while sitting on a pedestal track .
Revised: a lady riding a motorcycle and a street while sitting on a pedestal track .

Original: a
Revised: a

Original: commercial man
Revised: commercial man

Original: are standing
Revised: are standing

Original: in the bicycle next to the back of an orange computer an
Revised: in the bicycle next to the back of an orange computer an

Original: airplane
Revised: airplane

Original: the
Revised: the

Original: bus
Revised: bus

Original: is headed as something
Revised: is headed as something

Original: in
Revised: in

Original: the sky
Revised: the sky

Original: . - people riding on a bike on a surfer surfing doing the ocean .
Revised: . - people riding on a bike on a surfer surfing doing the ocean .

Original: - a plane propped up in front of a vehicle .
Revised: - a plane propped up in front of a vehicle .

Original: - a man wearing a cat in front of a motorcycle
Revised: - a man wearing a cat in front of a motorcycle

Original: EMNLP 2017 News
Revised: EMNLP 2017 News

Original: - a one - game event and a limited number this dropped by 38 per cent to find out what we could want , to be as much done as we finish as the games .
Revised: - a one - game event and a limited number this dropped by 38 per cent to find out what we could want , to be as much done as we finish as the games .

Original: - more than 30 per cent of calais camps at risk were taken between children and child refugees crossing the mediterranean to fall apart .
Revised: - more than 30 per cent of calais camps at risk were taken between children and child refugees crossing the mediterranean to fall apart .

Original: - the uk government got on the deal when it came to other eu countries that allowed workplace checks for 26 .
Revised: - the uk government got on the deal when it came to other eu countries that allowed workplace checks for 26 .

Original: - so women i didn ’ t want my parents to work hard with me at the time of the task of making it presents .
Revised: - so women i didn ’ t want my parents to work hard with me at the time of the task of making it presents .

Original: - black voters feel there may not be enough choice between former president george w .
Revised: - black voters feel there may not be enough choice between former president george w .

Original: bush and i have to go along with problems and support the people that are in a swing state for president .
Revised: bush and i have to go along with problems and support the people that are in a swing state for president .

Original: - he sold out to its eighth list , but a stunning showing that what cost the ability to start the league failed ? - let ’ s catch him down his back phone one morning john f kennedy , where he is in a us meeting group with supporters of love not being successful in the fields .
Revised: - he sold out to its eighth list , but a stunning showing that what cost the ability to start the league failed ? - let ’ s catch him down his back phone one morning john f kennedy , where he is in a us meeting group with supporters of love not being successful in the fields .

Original: - then there ’ s a couple of chances that they won competition - - the result from what is of me going out there that i really will play for the foundation .
Revised: - then there ’ s a couple of chances that they won competition - - the result from what is of me going out there that i really will play for the foundation .

Original: COCO
Revised: COCO

Original: - a black motorcycle parked next to a river .
Revised: - a black motorcycle parked next to a river .

Original: - a bathroom with a mirror , a shower and a toilet .
Revised: - a bathroom with a mirror , a shower and a toilet .

Original: - two people sit on a building in front of a building .
Revised: - two people sit on a building in front of a building .

Original: - a group of people walking around the corner of a building .
Revised: - a group of people walking around the corner of a building .

Original: - people are riding bikes on a city street .
Revised: - people are riding bikes on a city street .

Original: - there is a picture of a bathroom in a bathroom . -
Revised: - there is a picture of a bathroom in a bathroom . -

Original: an empty city street with cars parked on the side of the
Revised: an empty city street with cars parked on the side of the

Original: road .
Revised: road .

Original: - a man taking a picture of a white bathroom .
Revised: - a man taking a picture of a white bathroom .

Original: EMNLP 2017 News
Revised: EMNLP 2017 News

Original: - if i go to the party , it ’ s a disaster , and i ’ m going to keep it right .
Revised: - if i go to the party , it ’ s a disaster , and i ’ m going to keep it right .

Original: - i think i ’ ve had some great strength in my life and i want that to be here for me .
Revised: - i think i ’ ve had some great strength in my life and i want that to be here for me .

Original: - obama pointed to reporters claiming he was willing to discuss the situation , but the u .
Revised: - obama pointed to reporters claiming he was willing to discuss the situation , but the u .

Original: s .
Revised: s .

Original: have been military concern by american troops , including a nuclear deal in march .
Revised: have been military concern by american troops , including a nuclear deal in march .

Original: - a 16 - year - old man was arrested after the death of a man shot and arrested him on suspicion on his behalf .
Revised: - a 16 - year - old man was arrested after the death of a man shot and arrested him on suspicion on his behalf .

Original: - the problem is that they are hard to find out what they want to see where they are and what they can achieve their past two years .
Revised: - the problem is that they are hard to find out what they want to see where they are and what they can achieve their past two years .

Original: - now it ’ s important to me and i am thinking so hard to make it the best for a long time .
Revised: - now it ’ s important to me and i am thinking so hard to make it the best for a long time .

Original: - great britain is suggesting the u .
Revised: - great britain is suggesting the u .

Original: s . - led coalition figures at the australian foreign investor have raised more than $ 250 , 000 in the construction of operations .
Revised: s . - led coalition figures at the australian foreign investor have raised more than $ 250 , 000 in the construction of operations .

Original: - it is a simple way to believe that if it can happen , then i ’ m not the right choice .
Revised: - it is a simple way to believe that if it can happen , then i ’ m not the right choice .