Original: L ATENT T OPIC C ONVERSATIONAL M ODELS
Revised: L ATENT T OPIC C ONVERSATIONAL M ODELS

Original: Anonymous authors
Revised: Anonymous authors

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

Original: A BSTRACT
Revised: A BSTRACT

Original: Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses.
Revised: Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses.

Original: In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations.
Revised: In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations.

Original: The neural topic component encodes information from the source sentence to build a global “topic” distribution over words, which is then consulted by the seq2seq model to improve generation at each time step.
Revised: The neural topic component encodes information from the source sentence to build a global “topic” distribution over words, which is then consulted by the seq2seq model to improve generation at each time step.

Original: The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations.
Revised: The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations.

Original: In a subjective human evaluation, the judges also conﬁrm that LTCM is the preferred option comparing to competitive baseline models.
Revised: In a subjective human evaluation, the judges also conﬁrm that LTCM is the preferred option comparing to competitive baseline models.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Sequence-to-Sequence model (seq2seq) (Sutskever et al., 2014), as a data-driven approach to mapping between two arbitrary length sequences, has attracted much attention and been widely applied to many natural language processing tasks such as machine translation (Cho et al., 2014; Luong et al., 2015), syntactic parsing (Vinyals et al., 2015), and summarisation (Nallapati et al., 2016).
Revised: Sequence-to-Sequence model (seq2seq) (Sutskever et al., 2014), as a data-driven approach to mapping between two arbitrary length sequences, has attracted much attention and been widely applied to many natural language processing tasks such as machine translation (Cho et al., 2014; Luong et al., 2015), syntactic parsing (Vinyals et al., 2015), and summarisation (Nallapati et al., 2016).

Original: Neural conversational models (Vinyals & Le, 2015; Shang et al., 2015; Serban et al., 2016a) are the latest development in open-domain conversational modelling, where seq2seq-based models are employed for learning dialogue decisions in an end-to-end fashion.
Revised: Neural conversational models (Vinyals & Le, 2015; Shang et al., 2015; Serban et al., 2016a) are the latest development in open-domain conversational modelling, where seq2seq-based models are employed for learning dialogue decisions in an end-to-end fashion.

Original: Despite promising results, the lack of explicit knowledge representations (or the inability to learn them from data) impedes the model from generating causal or even rational responses.
Revised: Despite promising results, the lack of explicit knowledge representations (or the inability to learn them from data) impedes the model from generating causal or even rational responses.

Original: This leads to many problems discussed in previous literature such as generic responses (Li et al., 2016a), inconsistency (Li et al., 2016b), and redundancy and contradiction (Shao et al., 2017).
Revised: This leads to many problems discussed in previous literature such as generic responses (Li et al., 2016a), inconsistency (Li et al., 2016b), and redundancy and contradiction (Shao et al., 2017).

Original: On the other hand, goal-oriented dialogues (Young et al., 2013) use the notion of dialogue ontology to constrain the scope of conversation and facilitate rational system behaviour within the domain.
Revised: On the other hand, goal-oriented dialogues (Young et al., 2013) use the notion of dialogue ontology to constrain the scope of conversation and facilitate rational system behaviour within the domain.

Original: Neural network-based task-oriented dialogue systems usually retrieve knowledge from a pre-deﬁned database either by discrete accessing (Wen et al., 2017b; Bordes & Weston, 2017) or through an attention mechanism (Dhingra et al., 2017).
Revised: Neural network-based task-oriented dialogue systems usually retrieve knowledge from a pre-deﬁned database either by discrete accessing (Wen et al., 2017b; Bordes & Weston, 2017) or through an attention mechanism (Dhingra et al., 2017).

Original: The provision of this database offers a proxy for language grounding, which is crucial to guide the generation or selection of the system responses.
Revised: The provision of this database offers a proxy for language grounding, which is crucial to guide the generation or selection of the system responses.

Original: As shown in Wen et al.
Revised: As shown in Wen et al.

Original: 2017a, a stochastic neural dialogue model can generate diverse yet rational responses mainly because they are heavily driven by the knowledge the model is conditioned on.
Revised: 2017a, a stochastic neural dialogue model can generate diverse yet rational responses mainly because they are heavily driven by the knowledge the model is conditioned on.

Original: Despite the need for explicit knowledge representations, building a general-purpose knowledge base and actually making use of it have been proven difﬁcult (Matuszek et al., 2006; Miller et al., 2016).
Revised: Despite the need for explicit knowledge representations, building a general-purpose knowledge base and actually making use of it have been proven difﬁcult (Matuszek et al., 2006; Miller et al., 2016).

Original: Therefore, progress has been made in conditioning the seq2seq model on coarse-grained knowledge representations, such as a fuzzily-matched retrieval result via attention (Ghazvininejad et al., 2017) or a set of pre-organised topic or scenario labels (Wang et al., 2017; Xing et al., 2016).
Revised: Therefore, progress has been made in conditioning the seq2seq model on coarse-grained knowledge representations, such as a fuzzily-matched retrieval result via attention (Ghazvininejad et al., 2017) or a set of pre-organised topic or scenario labels (Wang et al., 2017; Xing et al., 2016).

Original: In this work, we propose a hybrid of a seq2seq conversational model and a neural topic model – Latent
Revised: In this work, we propose a hybrid of a seq2seq conversational model and a neural topic model – Latent

Original: Topic Conversational Model (LTCM) – to jointly learn the useful latent representations and the way to make use of them in a conversation.
Revised: Topic Conversational Model (LTCM) – to jointly learn the useful latent representations and the way to make use of them in a conversation.

Original: LTCM uses its underlying seq2seq model to capture the local dynamics of a sentence while extracts and represents its global semantics by a mixture of topic components like topic models (Blei et al., 2003).
Revised: LTCM uses its underlying seq2seq model to capture the local dynamics of a sentence while extracts and represents its global semantics by a mixture of topic components like topic models (Blei et al., 2003).

Original: This separation of global semantics and local dynamics turns out to be crucial to the success of LTCM.
Revised: This separation of global semantics and local dynamics turns out to be crucial to the success of LTCM.

Original: Recent advances in neural variational inference (Mnih & Gregor, 2014; Miao et al., 2016) have sparked a series of latent variable models applied to conversational modeling (Serban et al., 2016b;
Revised: Recent advances in neural variational inference (Mnih & Gregor, 2014; Miao et al., 2016) have sparked a series of latent variable models applied to conversational modeling (Serban et al., 2016b;

Original: Cao & Clark, 2017; Zhao et al., 2017).
Revised: Cao & Clark, 2017; Zhao et al., 2017).

Original: The majority of the work passes a Gaussian random variable to the hidden state of the LSTM decoder and employs the reparameterisation trick (Kingma & Welling, 2014) to build an unbiased and low-variance gradient estimator for updating the model parameters.
Revised: The majority of the work passes a Gaussian random variable to the hidden state of the LSTM decoder and employs the reparameterisation trick (Kingma & Welling, 2014) to build an unbiased and low-variance gradient estimator for updating the model parameters.

Original: However, studies have shown that training this type of models for language generation tasks is tough because the effect of the latent variable tends to vanish and the language model would take over the entire generation process over time (Bowman et al., 2015).
Revised: However, studies have shown that training this type of models for language generation tasks is tough because the effect of the latent variable tends to vanish and the language model would take over the entire generation process over time (Bowman et al., 2015).

Original: This results in several workarounds such as KL annealing (Bowman et al., 2015; Cao & Clark, 2017), word dropout and historyless decoding (Bowman et al., 2015), as well as auxiliary bag-of-word signals (Zhao et al., 2017).
Revised: This results in several workarounds such as KL annealing (Bowman et al., 2015; Cao & Clark, 2017), word dropout and historyless decoding (Bowman et al., 2015), as well as auxiliary bag-of-word signals (Zhao et al., 2017).

Original: Unlike previous approaches, LTCM is similar to TopicRNN (Dieng et al., 2017) where it passes the latent variable to the output layer of the decoder and only back-propagates the gradient of the topic words to the latent variable.
Revised: Unlike previous approaches, LTCM is similar to TopicRNN (Dieng et al., 2017) where it passes the latent variable to the output layer of the decoder and only back-propagates the gradient of the topic words to the latent variable.

Original: In summary, the contribution of this paper is two-fold: ﬁrst and most importantly, we show that
Revised: In summary, the contribution of this paper is two-fold: ﬁrst and most importantly, we show that

Original: LTCM can learn to generate more diverse and interesting responses by sampling from the learnt topic representations.
Revised: LTCM can learn to generate more diverse and interesting responses by sampling from the learnt topic representations.

Original: The results were conﬁrmed by a corpus-based evaluation and a human assessment; secondly, we conducted a series of experiments to understand the properties of seq2seq-based latent variables models better, which may serve as rules of thumb for future model development.
Revised: The results were conﬁrmed by a corpus-based evaluation and a human assessment; secondly, we conducted a series of experiments to understand the properties of seq2seq-based latent variables models better, which may serve as rules of thumb for future model development.

Original:  B ACKGROUND
Revised:  B ACKGROUND

Original: We present the necessary building blocks of the LTCM model.
Revised: We present the necessary building blocks of the LTCM model.

Original: We ﬁrst introduce the seq2seq-based conversational model and its latent variable variant, followed by an introduction of the neural topic models.
Revised: We ﬁrst introduce the seq2seq-based conversational model and its latent variable variant, followed by an introduction of the neural topic models.

Original: 2. S EQ 2S EQ C ONVERSATIONAL M ODEL
Revised: 2. S EQ 2S EQ C ONVERSATIONAL M ODEL

Original: In general, a seq2seq model (Sutskever et al., 2014) generates a target sequence given a source sequence using two Recurrent Network Networks (RNNs), one for encoding the source, another for decoding the target.
Revised: In general, a seq2seq model (Sutskever et al., 2014) generates a target sequence given a source sequence using two Recurrent Network Networks (RNNs), one for encoding the source, another for decoding the target.

Original: Given a user input u
Revised: Given a user input u

Original: = { x 1 , x 2 , ...
Revised: = { x 1 , x 2 , ...

Original: x U } in the conversational setting, the goal is to produce a machine response m =
Revised: x U } in the conversational setting, the goal is to produce a machine response m =

Original: { y 1 , y 2 , ...y M } that maximises the conditional probability m ∗ =
Revised: { y 1 , y 2 , ...y M } that maximises the conditional probability m ∗ =

Original: argmax m p ( m | u ) .
Revised: argmax m p ( m | u ) .

Original: The decoder of the seq2seq model is effectively an RNN language model which measures the likelihood of a sequence through a joint probability distribution,
Revised: The decoder of the seq2seq model is effectively an RNN language model which measures the likelihood of a sequence through a joint probability distribution,

Original: [Equation]
Revised: [Equation]

Original: The conditional probability is then modeled by an RNN,
Revised: The conditional probability is then modeled by an RNN,

Original: [Equation]
Revised: [Equation]

Original: where h t is the hidden state at step t and function f W h ( · ) is the hidden state update that can either be a vanilla RNN cell or a more complex cell like Long Short-term Memory (LSTM) (Hochreiter &
Revised: where h t is the hidden state at step t and function f W h ( · ) is the hidden state update that can either be a vanilla RNN cell or a more complex cell like Long Short-term Memory (LSTM) (Hochreiter &

Original: Schmidhuber, 1997).
Revised: Schmidhuber, 1997).

Original: The initial state of the decoder h 0 is initialised by a vector representation of the source sentence, which is taken from the last hidden state of the encoder h 

Revised: The initial state of the decoder h 0 is initialised by a vector representation of the source sentence, which is taken from the last hidden state of the encoder h

Original: = ˆ h U .
Revised: = ˆ h U .

Original: The encoder state update also follows Equation 3.
Revised: The encoder state update also follows Equation 3.

Original: While theoretically, RNN-based models can memorise arbitrarily long sequences if provided with sufﬁcient capacity, in practice even the improved version such as LSTM or GRU (Chung et al., 2014) encounter difﬁculties during optimisation (Bengio et al., 1994).
Revised: While theoretically, RNN-based models can memorise arbitrarily long sequences if provided with sufﬁcient capacity, in practice even the improved version such as LSTM or GRU (Chung et al., 2014) encounter difﬁculties during optimisation (Bengio et al., 1994).

Original: This inability to memorising long-term dependencies prevents the model from extracting useful sentence-level semantics.
Revised: This inability to memorising long-term dependencies prevents the model from extracting useful sentence-level semantics.

Original: As a consequence, the model tends to focus on the low-hanging fruit (language modelling) during optimisation and yields a suboptimal result.
Revised: As a consequence, the model tends to focus on the low-hanging fruit (language modelling) during optimisation and yields a suboptimal result.

Original: 2. L ATENT V
Revised: 2. L ATENT V

Original: [Table]
Revised: [Table]

Original: Latent variable conversational model (Serban et al., 2016b; Cao & Clark, 2017; Zhao et al., 2017) is a derivative of the seq2seq model in which it incorporates a latent variable ν at the sentence-level to inject stochasticity and diversity.
Revised: Latent variable conversational model (Serban et al., 2016b; Cao & Clark, 2017; Zhao et al., 2017) is a derivative of the seq2seq model in which it incorporates a latent variable ν at the sentence-level to inject stochasticity and diversity.

Original: The objective function of the latent variable model is
Revised: The objective function of the latent variable model is

Original: [Equation]
Revised: [Equation]

Original: where ν is usually chosen to be Gaussian distributed and passed to the decoder at every time step where we rewrite Equation 3 as h t
Revised: where ν is usually chosen to be Gaussian distributed and passed to the decoder at every time step where we rewrite Equation 3 as h t

Original: = f W h ( y t − 1 , h t − 1 , ν ) .
Revised: = f W h ( y t − 1 , h t − 1 , ν ) .

Original: Since the optimisation against Equationis intractable, we apply variational inference and alternatively optimise the variational lowerbound,
Revised: Since the optimisation against Equationis intractable, we apply variational inference and alternatively optimise the variational lowerbound,

Original: [Equation]
Revised: [Equation]

Original: where we introduce the inference network q ( ν | u, m ) , a surrogate of p ( ν | u ) , to approximate the true posterior during training.
Revised: where we introduce the inference network q ( ν | u, m ) , a surrogate of p ( ν | u ) , to approximate the true posterior during training.

Original: Based on Equation 5, we can then sample ν ∼ q ( ν | u, m ) and apply the
Revised: Based on Equation 5, we can then sample ν ∼ q ( ν | u, m ) and apply the

Original: Gaussian reparameterisation trick (Kingma & Welling, 2014) to calculate the gradients and update the parameters.
Revised: Gaussian reparameterisation trick (Kingma & Welling, 2014) to calculate the gradients and update the parameters.

Original: Although latent variable conversational models were able to generate diverse responses, its optimisation has been proven difﬁcult, and several tricks are needed to obtain a good result.
Revised: Although latent variable conversational models were able to generate diverse responses, its optimisation has been proven difﬁcult, and several tricks are needed to obtain a good result.

Original: Among these tricks, KL loss annealing is the most general and effective approach (Bowman et al., 2015).
Revised: Among these tricks, KL loss annealing is the most general and effective approach (Bowman et al., 2015).

Original: The main idea of KL annealing is, instead of optimising the full KL term during training, we gradually increase using a linear schedule.
Revised: The main idea of KL annealing is, instead of optimising the full KL term during training, we gradually increase using a linear schedule.

Original: This way, the model is encouraged to encode information cheaply in ν without paying huge KL penalty in the early stage of training.
Revised: This way, the model is encouraged to encode information cheaply in ν without paying huge KL penalty in the early stage of training.

Original: 2. N EURAL T OPIC M ODELS
Revised: 2. N EURAL T OPIC M ODELS

Original: Probabilistic topic models are a family of models that are used to capture the global semantics of a document set (Srivastava & Sahami, 2009).
Revised: Probabilistic topic models are a family of models that are used to capture the global semantics of a document set (Srivastava & Sahami, 2009).

Original: They can be used as a tool to organise, summarise, and navigate document collections.
Revised: They can be used as a tool to organise, summarise, and navigate document collections.

Original: As an unsupervised approach, topic models rely on counting word co-occurrence in the same document to group words into topics.
Revised: As an unsupervised approach, topic models rely on counting word co-occurrence in the same document to group words into topics.

Original: Therefore, each topic represents a word cluster which puts most of its mass (weight) on this subset of the vocabulary.
Revised: Therefore, each topic represents a word cluster which puts most of its mass (weight) on this subset of the vocabulary.

Original: Despite there are many probabilistic graphical topic models (Blei et al., 2003), we focus on neural topic models (Larochelle & Lauly, 2012; Miao et al., 2016) because they can be directly integrated into seq2seq model as a submodule of LTCM.
Revised: Despite there are many probabilistic graphical topic models (Blei et al., 2003), we focus on neural topic models (Larochelle & Lauly, 2012; Miao et al., 2016) because they can be directly integrated into seq2seq model as a submodule of LTCM.

Original: One neural topic model that is similar to LDA is the Gaussian-softmax neural topic model introduced by Miao et al. 2017.
Revised: One neural topic model that is similar to LDA is the Gaussian-softmax neural topic model introduced by Miao et al. 2017.

Original: The generation process works as following:
Revised: The generation process works as following:

Original: Draw a document-level latent vector ν ∼ N ( µ 0 , σ 0 2 ) .
Revised: Draw a document-level latent vector ν ∼ N ( µ 0 , σ 0 2 ) .

Original: Construct a document-level topic proportion vector θ = softmax ( W (cid:62) ν ) .
Revised: Construct a document-level topic proportion vector θ = softmax ( W (cid:62) ν ) .

Original: For each word y t in the document, (a) Draw a topic assignment z t ∼ Multinomial ( θ ) .
Revised: For each word y t in the document, (a) Draw a topic assignment z t ∼ Multinomial ( θ ) .

Original: (b) Draw a word y t ∼ Multinomial ( β z t ) .
Revised: (b) Draw a word y t ∼ Multinomial ( β z t ) .

Original: where β
Revised: where β

Original: = { β 1 , β 2 , ...β K } , β k is the word distribution of topic k , and µ 0 and σ 0 are the mean and variance of an isotropic Gaussian.
Revised: = { β 1 , β 2 , ...β K } , β k is the word distribution of topic k , and µ 0 and σ 0 are the mean and variance of an isotropic Gaussian.

Original: The likelihood of a document d = { y 1 , y 2 , ...
Revised: The likelihood of a document d = { y 1 , y 2 , ...

Original: y D } is therefore,
Revised: y D } is therefore,

Original: [Equation]
Revised: [Equation]

Original: Note that in the original LDA, both the θ and β areboth drawn from a Dirichlet prior.
Revised: Note that in the original LDA, both the θ and β are drawn from a Dirichlet prior.

Original: Gaussiansoftmax model, on the other hand, constrcuts θ from a draw from an isotropic Gaussian with parameters µ 0 and σ 0 , where as β is random initialised as a parameter of the network.
Revised: Gaussian-softmax model, on the other hand, constrcuts θ from a draw of an isotropic Gaussian with parameters µ 0 and σ 0 , where as β is random initialised as a parameter of the network.

Original: Like most of the topic models, Gaussian-softmax model makes the bag-of-words assumption where the word order is ignored.
Revised: Like most of the topic models, Gaussian-softmax model makes the bag-of-words assumption where the word order is ignored.

Original: This simple assumption sacriﬁces the ability to model local transitions between words and phrases in exchange for the capability to capture global semantics.
Revised: This simple assumption sacriﬁces the ability to model local transitions between words and phrases in exchange for the capability to capture global semantics.

Original: Therefore, although topic model could not be used as a conversational model itself, it is nevertheless a perfect ﬁt to a sentence-level semantic extractor alongside a seq2seq model to improve the global coherence of the generated responses.
Revised: Therefore, although topic model could not be used as a conversational model itself, it is nevertheless a perfect ﬁt to a sentence-level semantic extractor alongside a seq2seq model to improve the global coherence of the generated responses.

Original:  L ATENT T OPIC C ONVERSATIONAL M ODEL
Revised:  L ATENT T OPIC C ONVERSATIONAL M ODEL

Original: Model The proposed Latent Topic Conversational Model (LTCM) is a hybrid of the seq2seq conversational model and the neural topic model, as shown in Figure 1.
Revised: Model The proposed Latent Topic Conversational Model (LTCM) is a hybrid of the seq2seq conversational model and the neural topic model, as shown in Figure 1.

Original: The neural topic sub-component is responsible for extracting and mapping between the input and output global semantics so that the seq2seq submodule can focus on perfecting local dynamics of the sentence such as syntax and word order.
Revised: The neural topic sub-component is responsible for extracting and mapping between the input and output global semantics so that the seq2seq submodule can focus on perfecting local dynamics of the sentence such as syntax and word order.

Original: Given a user input u and a machine response m , the generative process of LTCM can be described as the following,
Revised: Given a user input u and a machine response m , the generative process of LTCM can be described as the following,

Original: Encode user prompt u into a vector representation u = g Γ ( u ) ∈ R d . 2.
Revised: Encode user prompt u into a vector representation u = g Γ ( u ) ∈ R d . 2.

Original: Draw a sentence-level latent vector ν ∼ p Λ ( ν | u ) .
Revised: Draw a sentence-level latent vector ν ∼ p Λ ( ν | u ) .

Original: Construct a sentence-level topic proportion vector θ = softmax ( W (cid:62) 1 ν ) ∈ R K . 4.
Revised: Construct a sentence-level topic proportion vector θ = softmax ( W (cid:62) 1 ν ) ∈ R K . 4.

Original: Initialise the decoder hidden state h 

Revised: Initialise the decoder hidden state h

Original: = ˆ h U , where ˆ h U is the last encoder state.
Revised: = ˆ h U , where ˆ h U is the last encoder state.

Original: Given y 1: t − 1 , for the t -th word y t in the response, (a) Update decoder hidden state h t =
Revised: Given y 1: t − 1 , for the t -th word y t in the response, (a) Update decoder hidden state h t =

Original: f W h ( y t − 1 , h t − 1 ) (b) Draw a topic word indicator l t ∼ Bernoulli ( sigmoid ( W (cid:62) 2 h t ))
Revised: f W h ( y t − 1 , h t − 1 ) (b) Draw a topic word indicator l t ∼ Bernoulli ( sigmoid ( W (cid:62) 2 h t ))

Original: (c) Draw a word y t ∼ p ( y t | h t , l t , θ ; β ) , where
Revised: (c) Draw a word y t ∼ p ( y t | h t , l t , θ ; β ) , where

Original: [Equation]
Revised: [Equation]

Original: where p ( ν | u ) = N ( µ ( u ) , σ 2 ( u )) is a parametric isotropic Gaussian with a mean and variance both condition on the input prompt µ ( u ) =
Revised: where p ( ν | u ) = N ( µ ( u ) , σ 2 ( u )) is a parametric isotropic Gaussian with a mean and variance both condition on the input prompt µ ( u ) =

Original: MLP ( u ) , σ ( u ) =
Revised: MLP ( u ) , σ ( u ) =

Original: MLP ( u ) .
Revised: MLP ( u ) .

Original: To combine the seq2seq model with the neural topic module, we adopt the hard-decision style from TopicRNN (Dieng et al., 2017) by introducing an additional random variable l t .
Revised: To combine the seq2seq model with the neural topic module, we adopt the hard-decision style from TopicRNN (Dieng et al., 2017) by introducing an additional random variable l t .

Original: The topic indicator l t is to decide whether or not to take the logits of the neural topic module into account.
Revised: The topic indicator l t is to decide whether or not to take the logits of the neural topic module into account.

Original: If l t = 0 , which indicates that y t is a stop-word, the topic vector θ would have no contribution to the ﬁnal output.
Revised: If l t = 0 , which indicates that y t is a stop-word, the topic vector θ would have no contribution to the ﬁnal output.

Original: However, if l t = 1 , then the topic contribution term β (cid:62) i θ is added to the output of the seq2seq model, where β i is the word-topic vector for the i -th vocabulary word.
Revised: However, if l t = 1 , then the topic contribution term β (cid:62) i θ is added to the output of the seq2seq model, where β i is the word-topic vector for the i -th vocabulary word.

Original: Although the topic word indicator l t is sampled during inference, during training it is treated as observed and can be produced by either a stop-word list or ranking words in the vocabulary by their inverse document frequencies.
Revised: Although the topic word indicator l t is sampled during inference, during training it is treated as observed and can be produced by either a stop-word list or ranking words in the vocabulary by their inverse document frequencies.

Original: This hard decision of l t is crucial for LTCM because it explicitly sets two gradient routes for the model: when l t = 1 the gradients are back-propagated to the entire network; otherwise, they only ﬂow through the seq2seq model.
Revised: This hard decision of l t is crucial for LTCM because it explicitly sets two gradient routes for the model: when l t = 1 the gradients are back-propagated to the entire network; otherwise, they only ﬂow through the seq2seq model.

Original: This is important because topic models are known to be bad at dealing with stop-words (Mimno et al., 2017).
Revised: This is important because topic models are known to be bad at dealing with stop-words (Mimno et al., 2017).

Original: Therefore, preventing the topic model to learn from stop-words can help the extraction of global semantics.
Revised: Therefore, preventing the topic model to learn from stop-words can help the extraction of global semantics.

Original: Finally, the logits of the seq2seq and neural topic model are combined through an additive procedure.
Revised: Finally, the logits of the seq2seq and neural topic model are combined through an additive procedure.

Original: This makes the gradient ﬂow more straightforward and the training of LTCM becomes easier 1 .
Revised: This makes the gradient ﬂow more straightforward and the training of LTCM becomes easier 1 .

Original: The parameters of LTCM can be denoted as Θ = { Γ , Λ , W 1 , W 2 , W h , V , β } where V = { v 1 , v 2 , ... v L } and L is the vocabulary size.
Revised: The parameters of LTCM can be denoted as Θ = { Γ , Λ , W 1 , W 2 , W h , V , β } where V = { v 1 , v 2 , ... v L } and L is the vocabulary size.

Original: During training, the observed variables are input u , output m , and the topic word indicators l 1: M .
Revised: During training, the observed variables are input u , output m , and the topic word indicators l 1: M .

Original: The parametric form of LTCM is therefore,
Revised: The parametric form of LTCM is therefore,

Original: [Equation]
Revised: [Equation]

Original: Inference As a direct optimisation of Equation 7 is intractable because it involves an integral over the continuous latent space, variational inference (Jordan et al., 1999) is applied to approximate the log-likelihood objective.
Revised: Inference As a direct optimisation of Equation 7 is intractable because it involves an integral over the continuous latent space, variational inference (Jordan et al., 1999) is applied to approximate the log-likelihood objective.

Original: The variational lowerbound of Equation 7 can therefore be derived as
Revised: The variational lowerbound of Equation 7 can therefore be derived as

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: where q ( θ | u, m ) is the inference network introduced during training to approximate the true posterior.
Revised: where q ( θ | u, m ) is the inference network introduced during training to approximate the true posterior.

Original: The neural variational inference framework (Mnih & Gregor, 2014; Miao et al., 2016) and
Revised: The neural variational inference framework (Mnih & Gregor, 2014; Miao et al., 2016) and

Original: the
Revised: the

Original: Gaussian reparameterisation trick (Kingma & Welling, 2014; Rezende et al., 2014) are then followed to construct q ( θ | u, m ) ,
Revised: Gaussian reparameterisation trick (Kingma & Welling, 2014; Rezende et al., 2014) are then followed to construct q ( θ | u, m ) ,

Original: [Equation]
Revised: [Equation]

Original: where Φ = { W a , Ω 1 , Ω 2 } is the new set of parameters introduced for the inference network, u b and m b are the bag-of-words representations for u and m , respectively.
Revised: where Φ = { W a , Ω 1 , Ω 2 } is the new set of parameters introduced for the inference network, u b and m b are the bag-of-words representations for u and m , respectively.

Original: Although q ( θ | u, m ) and p ( θ | u ) are both parameterised as an isotropic Gaussian distribution, the approximation q ( θ | u, m ) only functions during training by producing samples to compute the stochastic gradients, while p ( θ | u ) is the generative distribution that generates the required topic proportion vectors for composing the machine response.
Revised: Although q ( θ | u, m ) and p ( θ | u ) are both parameterised as an isotropic Gaussian distribution, the approximation q ( θ | u, m ) only functions during training by producing samples to compute the stochastic gradients, while p ( θ | u ) is the generative distribution that generates the required topic proportion vectors for composing the machine response.

Original:  E XPERIMENTS 
Revised:  E XPERIMENTS Dataset

Original: Dataset

Original: We assessed the performance of the LTCM using both a corpus-based evaluation and a human assessment.
Revised: We assessed the performance of the LTCM using both a corpus-based evaluation and a human assessment.

Original: The dataset used in the experiments is a subset of the data collected by Shao et al. 2017, which includes mainly the Reddit 2 data which contains about 1.7 billion messages ( million conversations).
Revised: The dataset used in the experiments is a subset of the data collected by Shao et al. 2017, which includes mainly the Reddit 2 data which contains about 1.7 billion messages ( million conversations).

Original: Given the large volume of the data, a random subset of 15 million singleturn conversations was selected for this experiment.
Revised: Given the large volume of the data, a random subset of 15 million singleturn conversations was selected for this experiment.

Original: To process the Reddit data, messages belonging to the same post are organized as a tree, a single-turn conversation is extracted merely by treating each parent node as a prompt and its corresponding child nodes as responses.
Revised: To process the Reddit data, messages belonging to the same post are organized as a tree, a single-turn conversation is extracted merely by treating each parent node as a prompt and its corresponding child nodes as responses.

Original: A length of 50 words was set for both the source and target sequences during preprocessing.
Revised: A length of 50 words was set for both the source and target sequences during preprocessing.

Original: Sentences with any nonRoman alphabet were also removed.
Revised: Sentences with any nonRoman alphabet were also removed.

Original: This ﬁlters out around 40% to 50% of the examples.
Revised: This ﬁlters out around 40% to 50% of the examples.

Original: A few standardizations were made via regular expressions such as mapping all valid numbers to < number > and web URLs to < url > .
Revised: A few standardizations were made via regular expressions such as mapping all valid numbers to < number > and web URLs to < url > .

Original: A vocabulary size of 30K was set for encoder, decoder, and the neural topic component.
Revised: A vocabulary size of 30K was set for encoder, decoder, and the neural topic component.

Original: Model
Revised: Model

Original: The LTCM model was implemented on the publicly available NMT 3 code base (Luong et al., 2017).
Revised: The LTCM model was implemented on the publicly available NMT 3 code base (Luong et al., 2017).

Original: Three model types were compared in the experiments, the vanilla seq2seq conversa-
Revised: Three model types were compared in the experiments, the vanilla seq2seq conversational model (S2S) (Vinyals & Le, 2015), the latent variable conversational model (LV-S2S) (Serban

Original: [Table]
Revised: [Table]

Original: tional model (S2S) (Vinyals & Le, 2015), the latent variable conversational model (LV-S2S) (Serban et al., 2016b; Cao & Clark, 2017), and the Latent Topic Conversational Model (LTCM).
Revised: et al., 2016b; Cao & Clark, 2017), and the Latent Topic Conversational Model (LTCM).

Original: For all the seq2seq components, a 4-layer LSTM with 500 hidden units was used for both the encoder and decoder.
Revised: For all the seq2seq components, a 4-layer LSTM with 500 hidden units was used for both the encoder and decoder.

Original: We used the GNMT style encoder (Wu et al., 2016) where the ﬁrst layer is a bidirectional
Revised: We used the GNMT style encoder (Wu et al., 2016) where the ﬁrst layer is a bidirectional

Original: LSTM, while the last three layers are unidirectional.
Revised: LSTM, while the last three layers are unidirectional.

Original: Residual connections were used (He et al., 2016) to ease the optimisation of deep networks.
Revised: Residual connections were used (He et al., 2016) to ease the optimisation of deep networks.

Original: Layer Normalisation (Ba et al.) was applied to all the LSTM cells to facilitate learning.
Revised: Layer Normalisation (Ba et al.) was applied to all the LSTM cells to facilitate learning.

Original: The batch size was 128, and a dropout rate of 0.2 was used.
Revised: The batch size was 128, and a dropout rate of 0.2 was used.

Original: The Adam optimiser (Kingma & Ba, 2014) with a ﬁxed annealing schedule was used to update the parameters.
Revised: The Adam optimiser (Kingma & Ba, 2014) with a ﬁxed annealing schedule was used to update the parameters.

Original: For the latent variable conversational model, we explored the KL annealing strategy as suggested in Bowman et al. 2015 where the KL loss is linearly increased and reaches to the full term after one training epoch.
Revised: For the latent variable conversational model, we explored the KL annealing strategy as suggested in Bowman et al. 2015 where the KL loss is linearly increased and reaches to the full term after one training epoch.

Original: In LTCM, the 300 words with the highest inverse document frequency are used as stop-words and the rest are treated as topic words.
Revised: In LTCM, the 300 words with the highest inverse document frequency are used as stop-words and the rest are treated as topic words.

Original: Both the mutual angular regularisation (Xie et al., 2016) and the l2 regularisation were applied to the β matrix during training.
Revised: Both the mutual angular regularisation (Xie et al., 2016) and the l2 regularisation were applied to the β matrix during training.

Original: Evaluation and Decoding
Revised: Evaluation and Decoding

Original: To build the development and testing sets, additional 20K sentence pairs were extracted and divided evenly.
Revised: To build the development and testing sets, additional 20K sentence pairs were extracted and divided evenly.

Original: For evaluation, ﬁve metrics were reported: the approximated perplexity, the variational lowerbound, the KL loss, the sentence uniqueness and the Zipf coefﬁcient (Cao & Clark, 2017) of the generated responses.
Revised: For evaluation, ﬁve metrics were reported: the approximated perplexity, the variational lowerbound, the KL loss, the sentence uniqueness and the Zipf coefﬁcient (Cao & Clark, 2017) of the generated responses.

Original: Because the exact perplexity of the latent variable models is hard to assess due to sampling, an approximated perplexity is reported as suggested in Dieng et al. 2017.
Revised: Because the exact perplexity of the latent variable models is hard to assess due to sampling, an approximated perplexity is reported as suggested in Dieng et al. 2017.

Original: For latent variable conversational models, the approximate distribution for computing perplexity is p ( y t| y 1:
Revised: For latent variable conversational models, the approximate distribution for computing perplexity is p ( y t

Original: [Equation]
Revised: [Equation]

Original: 1 , u [Equation] =
Revised: u )

Original: [Equation]
Revised: [Equation]

Original: t p ( y t | h t , ˆ ν ) , where ˆ ν is the mean estimate of ν .
Revised: t p ( y t | h t , ˆ ν ) , where ˆ ν is the mean estimate of ν .

Original: While for LTCM it is
Revised: While for LTCM it is

Original: [Equation]
Revised: [Equation]

Original: (11) where again ˆ θ is the mean estimate of θ .
Revised: (11) where again ˆ θ is the mean estimate of θ .

Original: Both latent variable model and LTCM used greedy decoding to make sure the diversity they produce comes from the latent variable.
Revised: Both latent variable model and LTCM used greedy decoding to make sure the diversity they produce comes from the latent variable.

Original: For seq2seq model, however, we explored both the greedy and random sampling strategies.
Revised: For seq2seq model, however, we explored both the greedy and random sampling strategies.

Original: Given a prompt, each model was requested to generate ﬁve responses.
Revised: Given a prompt, each model was requested to generate ﬁve responses.

Original: This leads to 50K generated responses for the testing set.
Revised: This leads to 50K generated responses for the testing set.

Original: The sentence uniqueness score and Zipf coefﬁcient 4 , which were introduced both by Cao & Clarkas proxies to evaluate sentence and lexicon diversity respectively, were computed on the generated responses.
Revised: The sentence uniqueness score and Zipf coefﬁcient 4 , which were introduced both by Cao & Clarkas proxies to evaluate sentence and lexicon diversity respectively, were computed.

Original: 4. C ORPUS - BASED E VALUATION R ESULT
Revised: 4. C ORPUS - BASED E VALUATION R ESULT

Original: The result of the corpus-based evaluation is presented in Table 1.
Revised: The result of the corpus-based evaluation is presented in Table 1.

Original: The ﬁrst block shows the performance of the baseline seq2seq model, either by greedy decoding or random sampling.
Revised: The ﬁrst block shows the performance of the baseline seq2seq model, either by greedy decoding or random sampling.

Original: Unsurprisingly, S2S-sample can generate much more diverse responses than S2S-greedy .
Revised: Unsurprisingly, S2S-sample can generate much more diverse responses than S2S-greedy .

Original: However, these[Table] [Table] responses are not of high quality as can be seen in the human assessment in the next section.
Revised: However, these responses are not of high quality as can be seen in the human assessment in the next section.

Original: One interesting observation is that the sentence uniqueness score of S2S-greedy is much lower than the expected (2.65% < 20 % 5 ).
Revised: One interesting observation is that the sentence uniqueness score of S2S-greedy is much lower than the ex- [Table] [Table] pected (2.65% < 20 % 5 ).

Original: This echoes the generic response problem mentioned in previous works (Li et al., 2016a; Serban et al., 2016b).
Revised: This echoes the generic response problem mentioned in previous works (Li et al., 2016a; Serban et al., 2016b).

Original: The second block demonstrates the result of the latent variable conversational models.
Revised: The second block demonstrates the result of the latent variable conversational models.

Original: As can be seen, neither sampling from a prior ( LV-S2S, p ( ν ) ) nor a conditional ( LV-S2S, p ( ν | u ) ) helps to beat the performance of the seq2seq model.
Revised: As can be seen, neither sampling from a prior ( LV-S2S, p ( ν ) ) nor a conditional ( LV-S2S, p ( ν | u ) ) helps to beat the performance of the seq2seq model.

Original: Although both models perform equally well in terms of perplexity and lowerbound, the likewise low uniqueness scores as seq2seq indicate that both of their latent variables collapse into a single mode and do not encode much information.
Revised: Although both models perform equally well in terms of perplexity and lowerbound, the likewise low uniqueness scores as seq2seq indicate that both of their latent variables collapse into a single mode and do not encode much information.

Original: This was also observed in Zhao et al. 2017 when training seq2seq-based latent variable models.
Revised: This was also observed in Zhao et al. 2017 when training seq2seq-based latent variable models.

Original: The KL annealed model LV-S2S, p ( ν | u ) , +A , as suggested by Bowman et al. 2015, can help to mitigate this problem and achieve a much higher uniqueness score (42.6%).
Revised: The KL annealed model LV-S2S, p ( ν | u ) , +A , as suggested by Bowman et al. 2015, can help to mitigate this problem and achieve a much higher uniqueness score (42.6%).

Original: The third block shows the result of the LTCM models.
Revised: The third block shows the result of the LTCM models.

Original: As can be seen, LTCM trades in its KL loss and variational lowerbound in exchange for a higher response diversity (higher uniqueness score and lower Zipf).
Revised: As can be seen, LTCM trades in its KL loss and variational lowerbound in exchange for a higher response diversity (higher uniqueness score and lower Zipf).

Original: Interestingly, although the lowerbound was substantially worse than the baselines, the conditional LTCM models ( LTCM, p ( θ | u ) and LTCM, p ( θ | u ) , +V ) can still reach comparable perplexities.
Revised: Interestingly, although the lowerbound was substantially worse than the baselines, the conditional LTCM models ( LTCM, p ( θ | u ) and LTCM, p ( θ | u ) , +V ) can still reach comparable perplexities.

Original: This indicates that most of the additional loss incurred by LTCM was to encode the discourse-level diversity into the latent variable and therefore may not be a bad idea.
Revised: This indicates that most of the additional loss incurred by LTCM was to encode the discourse-level diversity into the latent variable and therefore may not be a bad idea.

Original: Given that the latent variable of LTCM can encode more useful information, sampling from a conditional can therefore better tailor the neural topic component to the user prompt and produce more relevant responses ( LTCM, p ( θ ) v.s. LTCM, p ( θ | u ) ).
Revised: Given that the latent variable of LTCM can encode more useful information, sampling from a conditional can therefore better tailor the neural topic component to the user prompt and produce more relevant responses ( LTCM, p ( θ ) v.s. LTCM, p ( θ | u ) ).

Original: Overall speaking, LTCM can generate more diverse responses comparing to baselines by encoding more information into the latent space.
Revised: Overall speaking, LTCM can generate more diverse responses comparing to baselines by encoding more information into the latent space.

Original: However, the slightly higher lowerbound and KL loss do not necessarily mean that the quality of the responses is worse.
Revised: However, the slightly higher lowerbound and KL loss do not necessarily mean that the quality of the responses is worse.

Original: More discussions follow in the next section.
Revised: More discussions follow in the next section.

Original: 4. H UMAN E VALUATION
Revised: 4. H UMAN E VALUATION

Original: Due to the difﬁculty in evaluating conversational agents (Liu et al., 2016; Wen et al., 2015), a human evaluation is usually necessary to assess the performance of the models.
Revised: Due to the difﬁculty in evaluating conversational agents (Liu et al., 2016; Wen et al., 2015), a human evaluation is usually necessary to assess the performance of the models.

Original: To do a less biased evaluation, a set of judges ( ∼ 250) were recruited on AMT.
Revised: To do a less biased evaluation, a set of judges ( ∼ 250) were recruited on AMT.

Original: For each task (a prompt), two randomly selected models were paired and each of them was asked to generate ﬁve responses given the prompt.
Revised: For each task (a prompt), two randomly selected models were paired and each of them was asked to generate ﬁve responses given the prompt.

Original: There is a total of 5000 comparisons randomly split between all pairs.
Revised: There is a total of 5000 comparisons randomly split between all pairs.

Original: This results in approximately 90 experiments per pair of comparison.
Revised: This results in approximately 90 experiments per pair of comparison.

Original: The number of tasks that each judge can do is capped to 20.
Revised: The number of tasks that each judge can do is capped to 20.

Original: To consider the response diversity, each judge was asked to rate each of the ﬁve generated responses from 1 to 5 based on the interestingness and appropriateness scores.
Revised: To consider the response diversity, each judge was asked to rate each of the ﬁve generated responses from 1 to 5 based on the interestingness and appropriateness scores.

Original: The quality assessment is shown in Table 2.
Revised: The quality assessment is shown in Table 2.

Original: The numbers inside the brackets are calculated by averaging the[Table] mean of the generated responses across prompts, while the ones outside the brackets are the average of the maximum scores across prompts.
Revised: The numbers inside the brackets are calculated by averaging the mean of the generated responses across prompts, while the ones outside the brackets are the average of the maximum scores across prompts.

Original: Moreover, at the end of the task, the judge was also asked to state a preference between the two systems.
Revised: Moreover, at the end of the task, the judge was also asked to state a preference between the two systems.

Original: The result is shown in Table 
Revised: The result is shown in Table 3.

Original: [Table]
Revised: [Table]

Original:  2 shows that the average scores (numbers inside the brackets) of S2S-greedy , LV-S2S, p ( ν | u ) , +A , and LTCM, p ( θ | u ) are pretty much the same (with the appropriateness of S2S-greedy slightly better).
Revised: Table 2 shows that the average scores (numbers inside the brackets) of S2S-greedy , LV-S2S, p ( ν | u ) , +A , and LTCM, p ( θ | u ) are pretty much the same (with the appropriateness of S2S-greedy slightly better).

Original: However, the maximum scores (numbers outside the brackets) show that LTCM is the best among the four ( interestingness : 3.97 and appropriateness : 4.04).
Revised: However, the maximum scores (numbers outside the brackets) show that LTCM is the best among the four ( interestingness : 3.97 and appropriateness : 4.04).

Original: This indicates that although
Revised: This indicates that although

Original: LTCM can generate pretty good responses, it could also produce sentences that are quite bad.
Revised: LTCM can generate pretty good responses, it could also produce sentences that are quite bad.

Original: This variance in response quality could be beneﬁcial if reinforcement learning is introduced to ﬁne-tune the latent variable (Wen et al., 2017a).
Revised: This variance in response quality could be beneﬁcial if reinforcement learning is introduced to ﬁne-tune the latent variable (Wen et al., 2017a).

Original: Table 3 shows the result of pairwise preference test between four models.
Revised: Table 3 shows the result of pairwise preference test between four models.

Original: As can be seen, LTCM is the preferred option for most of the judges when compared to other approaches.
Revised: As can be seen, LTCM is the preferred option for most of the judges when compared to other approaches.

Original: Table 4 shows a few examples for qualitative analysis of the models.
Revised: Table 4 shows a few examples for qualitative analysis of the models.

Original: As shown in the table, LTCM can generate more diverse and interesting responses comparing to the baseline methods.
Revised: As shown in the table, LTCM can generate more diverse and interesting responses comparing to the baseline methods.

Original: The diversity can be found at both the semantic and the syntactic level.
Revised: The diversity can be found at both the semantic and the syntactic level.

Revised: Figure 2 shows the analysis of the topic gate.

Revised: As can be seen, the learned gate corresponds to the human intuition and helps to coordinate the contribution of the two models while generating.

Original: For more examples of the generated responses, please refer to Appendix A.
Revised: For more examples of the generated responses, please refer to Appendix A.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this paper, we have proposed the Latent Topic Conversational Model (LTCM) for general-purpose conversational modeling.
Revised: In this paper, we have proposed the Latent Topic Conversational Model (LTCM) for general-purpose conversational modeling.

Original: We have shown that LTCM can generate more interesting and diverse responses by combining the seq2seq model and neural topic model so that global semantic representations and local word transitions can be modeled separately but learned jointly.
Revised: We have shown that LTCM can generate more interesting and diverse responses by combining the seq2seq model and neural topic model so that global semantic representations and local word transitions can be modeled separately but learned jointly.

Original: Both a corpus-based evaluation and a human assessment conﬁrm this ﬁnding.
Revised: Both a corpus-based evaluation and a human assessment conﬁrm this ﬁnding.

Original: Future work would be to study the learned representations and use them to control the meaning of the generated responses.
Revised: Future work would be to study the learned representations and use them to control the meaning of the generated responses.

Original: A E XAMPLE R ESPONSES
Revised: A E XAMPLE R ESPONSES

Original: [Table]
Revised: [Table]

Original: a reality
Revised: a reality

Original: [Table]
Revised: [Table]

Original: going
Revised: going

Original: [Table]
Revised: [Table]

Original: a lake
Revised: a lake

Original: [Table]
Revised: [Table]

Original: i bet it ’s a lot of fun to drive through there .
Revised: i bet it ’s a lot of fun to drive through there .

Original: [Table]
Revised: [Table]

Original: i love this place . . .
Revised: i love this place . . .

Original: i ’ve been there a few times .
Revised: i ’ve been there a few times .

Original: it ’s fucking amazing .
Revised: it ’s fucking amazing .

Original: [Table]
Revised: [Table]

Original: i was there last year . . .
Revised: i was there last year . . .

Original: it was really nice .
Revised: it was really nice .

Original: [Table]
Revised: [Table]

Original: i wish i could have been there .
Revised: i wish i could have been there .

Original: i was in the < unk >
Revised: i was in the < unk >

Original: and it was pretty cool .
Revised: and it was pretty cool .

Original: [Table]
Revised: [Table]

Original: wow , that ’s a lot of water .
Revised: wow , that ’s a lot of water .

Original: [Table]
Revised: [Table]

Original: i love this place .
Revised: i love this place .

Original: [Table]
Revised: [Table]

Original: wow , great shot !
Revised: wow , great shot !

Original: [Table]
Revised: [Table]

Original: wow , that ’s beautiful .
Revised: wow , that ’s beautiful .

Original: thanks for the post op .
Revised: thanks for the post op .

Original: [Table]
Revised: [Table]

Original: wow , that looks amazing .
Revised: wow , that looks amazing .

Original: * wow , that looks amazing .
Revised: * wow , that looks amazing .

Original: thanks for sharing .
Revised: thanks for sharing .

Original: [Table]
Revised: [Table]

Original: ( spoilers ) what are your season ﬁnale predictions ?
Revised: ( spoilers ) what are your season ﬁnale predictions ?

Original: [Table]
Revised: [Table]

Original: * i ’m going to be a < unk > fan .
Revised: * i ’m going to be a < unk > fan .

Original: [Table]
Revised: [Table]

Original: i think the < unk > will be the last one . . .
Revised: i think the < unk > will be the last one . . .

Original: * i think the < unk > will be the next < unk > .
Revised: * i think the < unk > will be the next < unk > .

Original: * i think the < unk > will be the one to go .
Revised: * i think the < unk > will be the one to go .

Original: [Table]
Revised: [Table]

Original: i think it ’s a pretty good idea .
Revised: i think it ’s a pretty good idea .

Original: * i think it ’s going to be a tie between the < unk > and < unk > .
Revised: * i think it ’s going to be a tie between the < unk > and < unk > .

Original: * jon < unk > dies .
Revised: * jon < unk > dies .

Original: * jon snow will be dead . * jon snow will die .
Revised: * jon snow will be dead . * jon snow will die .

Original: [Table]
Revised: [Table]

Original: < number > miles to my local disk golf course .
Revised: < number > miles to my local disk golf course .

Original: what a beautiful day < smile >
Revised: what a beautiful day < smile >

Original: [Table]
Revised: [Table]

Original: * i ’m jealous .
Revised: * i ’m jealous .

Original: i ’m jealous .
Revised: i ’m jealous .

Original: [Table]
Revised: [Table]

Original: A * i ’m jealous .
Revised: A * i ’m jealous .

Original: * i ’m jealous . .
Revised: * i ’m jealous . .

Original: [Table]
Revised: [Table]

Original: congrats
Revised: congrats

Original: [Table] [Table]
Revised: [Table] [Table]

Original: sad
Revised: sad

Original: [Table]
Revised: [Table]

Original: i always get really excited when i see this .
Revised: i always get really excited when i see this .

Original: * i hope he ’s okay .
Revised: * i hope he ’s okay .

Original: * i need to get a job .
Revised: * i need to get a job .

Original: * i really like this .
Revised: * i really like this .

Original: thanks for sharing .
Revised: thanks for sharing .

Original: * i want to see this .
Revised: * i want to see this .

Original: [Table]
Revised: [Table]

Original: * i love this .
Revised: * i love this .

Original: * oh god , i love this guy .
Revised: * oh god , i love this guy .

Original: * oh my god , i love this .
Revised: * oh my god , i love this .

Original: * oh my god , i love this guy .
Revised: * oh my god , i love this guy .

Original: * oh my god , yes !
Revised: * oh my god , yes !