Original: F INDING R E MO (R ELATED M EMORY O BJECT ): A S IMPLE N EURAL A RCHITECTURE FOR T EXT BASED R EASONING
Revised: F INDING R E MO (R ELATED M EMORY O BJECT ): A S IMPLE N EURAL A RCHITECTURE FOR T EXT BASED R EASONING

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: Memory Network based models have shown a remarkable progress on  the task of relational reasoning.
Revised: To solve the text-based question and answering task that requires relational reasoning, it is necessary to memorize a large amount of information and ﬁnd out the question relevant information from the memory. Most approaches were based on external memory and four components proposed by Memory Network. The distinctive component among them was the way of ﬁnding the necessary information and it contributes to the performance.

Original: Recently, a simpler yet powerful neural network module  called Relation Network (RN) has been introduced.
Revised: Recently, a simple but powerful neural network module for reasoning called Relation Network (RN) has been introduced.

Original: Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory.
Revised: We analyzed RN from the view of Memory Network, and realized that its MLP component is able to reveal the complicate relation between question and object pair.

Original: We introduce Related Memory Network , an end-to-end neural network architecture exploiting both memory network and relation network structures. We follow memory network’s four components while each component operates similar to the relation network without taking a pair of objects. As a result, our model is as simple as RN but the computational complexity is reduced to linear time.
Revised: Motivated from it, we introduce Relation Memory Network (RMN) which uses MLP to ﬁnd out relevant information on Memory Network architecture.

Original: It achieves the stateof-the-art results in jointly trained bAbI-10k story-based question answering  and bAbI dialog dataset.
Revised: It shows new state-of-the-art results in jointly trained bAbI-10k story-based question answering tasks and bAbI dialog-based question answering tasks.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Neural network has made an enormous progress on the two major challenges in artiﬁcial intelligence: seeing and reading.
Revised: Neural network has made an enormous progress on the two major challenges in artiﬁcial intelligence: seeing and reading.

Original: In both areas, embedding methods have served as the main vehicle to process and analyze text and image data for solving classiﬁcation problems.
Revised: In both areas, embedding methods have served as the main vehicle to process and analyze text and image data for solving classiﬁcation problems.

Original: As for the task of logical reasoning, however, more complex and careful handling of features is called for.
Revised: As for the task of logical reasoning, however, more complex and careful handling of features is called for.

Original: A reasoning task requires the machine  answer a simple question upon the delivery of a series of sequential information.
Revised: A reasoning task requires the machine to answer a simple question upon the delivery of a series of sequential information.

Original: For example, imagine that the machine is given the following three questions: “Mary got the milk there.”
Revised: For example, imagine that the machine is given the following three sentences: “Mary got the milk there.”

Original: , “John moved to the bedroom.”, and “Mary traveled to the hallway.”
Revised: , “John moved to the bedroom.”, and “Mary traveled to the hallway.”

Original: Once prompted with the question, ”Where is the milk?”
Revised: Once prompted with the question, “Where is the milk?”

Original: , the machine then needs to sequentially focus on the two  sentences, ”Mary got the milk there” and ”Mary traveled to the hallway” in order to successfully determine that the milk is located in the hallway.
Revised: , the machine then needs to sequentially focus on the two supporting sentences, “Mary got the milk there.” and “Mary traveled to the hallway.” in order to successfully determine that the milk is located in the hallway.

Original: Inspired by this reasoning mechanism, J. Weston & Bordes (2015) has introduced the memory network (MemNNs), which consists of an external memory and four components: input feature map (I), generalization (G), output feature map (O), and response (R).
Revised: Inspired by this reasoning mechanism, J. Weston & Bordes (2015) has introduced the memory network (MemNN), which consists of an external memory and four components: input feature map ( I ), generalization ( G ), output feature map ( O ), and response ( R ).

Original: The external memory enables the model to deal with a knowledge base without loss of information.
Revised: The external memory enables the model to deal with a knowledge base without loss of information.

Original: Input feature map embeds the incoming sentences; Generalization updates old memories given the new input; Output feature map ﬁnds relevant information from the memory, and; Response produces the ﬁnal output.
Revised: Input feature map embeds the incoming sentences. Generalization updates old memories given the new input and output feature map ﬁnds relevant information from the memory. Finally, response produces the ﬁnal output.

Original: End-toend memory networks (MemN2N) (Sukhbaatar et al., 2015), gated end-to-end memory networks (GMemN2N)
Revised: Based on the memory network architecture, neural network based models like end-to-end memory network (MemN2N) (Sukhbaatar et al., 2015), gated end-to-end memory network (GMemN2N)

Original: (Liu & Perez, 2017) , dynamic memory networks (DMN) (Kumar et al., 2016), and dynamic memory network + (DMN+) (Xiong et al., 2016) are all based on memory network architecture.
Revised: (Liu & Perez, 2017), dynamic memory network (DMN) (Kumar et al., 2016), and dynamic memory network + (DMN+) (Xiong et al., 2016) are proposed. Since strong reasoning ability depends on whether the model is able to sequentially catching the right supporting sentences that lead to the answer, the most important thing that discriminates those models is the way of constructing the output feature map. As the output feature map becomes more complex, it is able to learn patterns for more complicate relations. For example, MemN2N, which has the lowest performance among the four models, measures the relatedness between question and sentence by the inner product, while the best performing DMN+ uses inner product and absolute difference with two embedding matrices.

Original: Recently, a new architecture called Relation Networks (RNs) (Santoro et al., 2017) has been proposed as a general solution to relational reasoning.
Revised: Recently, a new architecture called Relation Network (RN) (Santoro et al., 2017) has been proposed as a general solution to relational reasoning.

Original: While DMN, DMN+, GMemN2N has to go throughcomplicated computation process, RN simply learns from the compositions of two multilayer perceptrons (MLPs) yet achieve better performance as compared to the previous three models. RN’s simplicity origins from its unique input form which is a set of objects.
Revised: The design philosophy behind it is to directly capture the supporting relation between the sentences through the multi-layer perceptron (MLP). Despite its simplicity, RN achieves better performance than previous models without any catastrophic failure. The interesting thing we found is that RN can also be interpreted in terms of MemNN.

Original: The design philosophy behind RNs is  to to directly capture the relation between the object pair. However, since it considers a pair at a time, the computation complexity becomes quadratic.
Revised: It is composed of O and R where each corresponds to MLP which focuses on the related pair and another MLP which infers the answer. RN does not need to have G because it directly ﬁnds all the supporting sentences at once.

Original: Quadratic complexity limits the number of pairs of objects that the model can handle, hence making it difﬁcult for the model to be augmented with large scale external memory.
Revised: In this point of view, the signiﬁcant component would be MLP-based output feature map. As MLP is enough to recognize highly non-linear pattern, RN could ﬁnd the proper relation better than previous models to answer the given question.

Original: Santoro et al. (2017) has suggested attention mechanisms as  a solution to ﬁlter out unimportant relations; however, since it interrupts the reasoning operation, it may not be the most optimal solution to the problem. We propose “Related Memory Networks” (RMNs) as an alternative mechanism to effectively solve the reasoning task.
Revised: However, as RN considers a pair at a time unlike MemNN, the number of relations that RN learns is n 2 when the number of input sentence is n . When n is small, the cost of learning relation is reduced by n times compared to MemNN based models, which enables more data-efﬁcient learning (Santoro et al., 2017).

Original: The architecture is inspired by the close connection between memory network and relation network. RMN is as simple as RN, yet its computational complexity is reduced to O ( n ) . It consists of an external memory and four components: embedding, attention, updating, and reasoning. Each component is composed of a MLP, making RMN end-to-end differentiable.
Revised: However, when n increases, the performance becomes worse than the previous models.

Original: Experiment results show that RMN achieves state-of-the-art accuracy on bAbI story-based question answering and bAbI dialog dataset.
Revised: In this case, the pair-wise operation increases the number of non-related sentence pairs more than the related sentence pair, thereby confuses RN’s learning.

Original: To answer the question from a given set of facts, the model needs to memorize these facts from the past. Long short term memory (LSTM) (Hochreiter & Schmidhuber, 1997), one of the variants of recurrent neural network (RNN), is inept at remembering past stories because of their small internal memory (Sukhbaatar et al., 2015).
Revised: Santoro et al.

Original: To cope with this problem, J. Weston & Bordes (2015) has proposed a new class of memory-augmented model called Memory Network (MemNN). MemNN comprises of an external memory m and four components: input feature map ( I ), generalization ( G ), output feature map ( O ), and response ( R ). I encodes the input x to I ( x ) and G updates the memory given the I ( x ) ( m = G ( m, I ( x )) ), whereas O reads output feature o from the memory ( o = O
Revised: (2017) has suggested attention mechanisms as a solution to ﬁlter out unimportant relations; however, since it interrupts the reasoning operation, it may not be the most optimal solution to the problem.

Original: ( I ( x ) , m ) ). Finally, R infers an answer from o . MemN2N, GMemN2N, DMN, and DMN+ closely resembles MemNN’s structure, in which each component is  a neural network that can be trained end-to-end. Differences between these models comes from the way the components are constructed.
Revised: Our proposed model, “Relation Memory Network” (RMN), is able to ﬁnd complex relation even when a lot of information is given.

Original: MemN2N consists of I , O , and R components without G . O ﬁrst computes output feature o based on the inner product of the embedded facts and the prompt question. R produces an answer given the sum of the output vector o and the embedded question. GMemN2N exhibits a similar structure as
Revised: It uses MLP to ﬁnd out relevant information with a new generalization which simply erase the information already used.

Original: MemN2N, except that R yields an answer from the gated sum of the o and the embedded question. DMN and DMN+ are made up of all four components–I, G, O, and R–where G and O are modelled in a more delicate manner. They calculate o from the concatenation of a variety of similarities between input ( c ), memory ( m ) and question ( q ) vectors, such as c ◦ q , c ◦ m , | c − q | , | c − m | and so on. G updates memory with gated recurrent units (GRUs) (Chung et al., 2014).
Revised: In other words, RMN inherits RN’s

Original: Performance of these models are sensitive to how ﬁnely each component is constructed. in constrast, RMN uses simpler MLPs so model sensitivity is reduced, while showing better performance.
Revised: MLP-based output feature map on Memory Network architecture. Experiments show its state-ofthe-art result on the text-based question answering tasks.

Original: 2.  R ELATION  N ETWORK
Revised:  R ELATION M EMORY N ETWORK

Original: Unlike memory-augmented neural network models which select and update necessary information in a complex and sophisticated manner, Relation Networks (RN) have emerged as a new and simpler framework for solving the inference problem.

Original: Under the assumption that relations are inferred from the connection between an object and another, RN takes in a pair of objects and the prompt question as its input, of which the mathematical expressions is as following:

Original: [Equation] where g θ and f φ are MLPs with parameters θ and φ . The output of g θ is relation, whether two objects are related in or not, and f φ aggregates all these outputs toinfer the answer. RN has an advantage of solving the task through a MLP without any other complicated processes, but it requires O ( n 2 ) complexity for computation because the model has to concatenate two objects together as a pair.
Revised: 18 Mary travelled to the kitchen.

Original: Santoro et al. (2017) has pointed out that that prior knowledge could be reﬂected at this point and suggested employing attentional mechanisms in order to ﬁlter out unimportant [Figure] performance.
Revised: 146 Daniel travelled to the hallway.

Original: For example, suppose that we compute an attention weight α from a given attention module. The weighted sum of the α i toeach o i , regardless of the quality of the attention model employed, blurs the relation between the set of objects.  M ODEL 3. O UTPUT FEATURE MAP AND g θ
Revised: Daniel went back to the garden.

Original: We found a close connection between output feature map ( O ) and g θ used in RN. O component retrieves the output vector through the attention weight between 0 and 1. It indicates how relevant is the story to the question and can be calculated via cosine similarity or concatenation of many interactions between them. g θ also ﬁnds meaningful relation with the given question.
Revised: 148 Where was the apple before the garden?

Original: Different from O , this simple MLP directly yields the output vector implying whether the input objects are related or not, as seen in ﬁgure 1. Motivated by the potential of MLP to focus on the related object, we use MLP to produce the attention weight without any extrinsic computation between the input sentence and the question. By virtue of little modiﬁcation, RMN was able to be a simple but strong model with reduced time complexity.{+(cid:1) (cid:1) 
+}
Revised: {+(cid:1) (cid:1) 
+}

Original: [Figure]
Revised: Daniel grabbed the apple. 145 Mary went to the bedroom. [Table]

Original: (cid:17)(cid:4)(cid:6)(cid:8)(cid:14)(cid:10)(cid:4)(cid:5)(cid:18)(cid:19)(cid:20)(cid:2) (cid:21)(cid:15)(cid:5)(cid:22)(cid:10)(cid:6)(cid:7)(cid:8) (cid:23)(cid:4)(cid:22)(cid:24)(cid:11)(cid:7)(cid:6)(cid:7)(cid:8) (cid:9)(cid:10)(cid:10)(cid:4)(cid:7)(cid:10)(cid:6)(cid:11)(cid:7)(cid:12)(cid:25)(cid:14)(cid:11)(cid:15)(cid:16) (cid:1) (cid:2) (cid:3) (cid:1) (cid:3) (cid:2) (cid:2) (cid:4) (cid:3) (cid:4) (cid:3)(cid:4)(cid:1) (cid:4) (cid:3)(cid:4)(cid:2) (cid:4) (cid:3)(cid:4)(cid:5) (cid:4) (cid:1) (cid:4) (cid:3) (cid:4) (cid:3)(cid:4)(cid:1) (cid:4) (cid:3)(cid:4)(cid:2) (cid:4) (cid:3)(cid:4)(cid:5) (cid:4) (cid:1) (cid:1)(cid:2)(cid:1)
Revised: (cid:4) (cid:3)(cid:4)(cid:1) (cid:4) (cid:3)(cid:4)(cid:2) (cid:4) (cid:3)(cid:4)(cid:5) (cid:4) (cid:1) (cid:4) (cid:3) (cid:4) (cid:3)(cid:4)(cid:1) (cid:4) (cid:3)(cid:4)(cid:2) (cid:4) (cid:3)(cid:4)(cid:5) (cid:4) (cid:1) (cid:1)(cid:2)(cid:1)

Original: Our model, Related Memory Network (RMN), is composed of four components - embedding, attention, updating, and reasoning.
Revised: Relation Memory Network (RMN) is composed of four components - embedding, attention, updating, and reasoning.

Original: It takes  the inputs a set of stories x 1 , x 2 , ..., x n and its related question q , and outputs an answer a .
Revised: It takes as the inputs a set of sentences x 1 , x 2 , ..., x n and its related question u , and outputs an answer a .

Original: Each of the x i , q , and a is made up of one-hot representation of words, for example, x i = { x i 1 , x i 2 , x i 3 , ..., x in i } ( x ij ∈ R V , j = (1 , 2 , ..., n i ) , V = vocabulary size , n i = number of words in sentence i ) .
Revised: Each of the x i , u , and a is made up of one-hot representation of words, for example, x i = { x i 1 , x i 2 , x i 3 , ..., x in i } ( x ij ∈ R V , j = (1 , 2 , ..., n i ) , V = vocabulary size , n i = number of words in sentence i ) .

Original: Stories are stored in the memory and ﬂow to the following components.

Original: Embedding component
Revised: 2. E MBEDDING COMPONENT

Original: We ﬁrst embed words in each x i
Revised: We ﬁrst embed words in each x i

Original: = { x i 1 , x i 2 , x i 3 , ..., x in i } and q to a continuous space multiplying an embedding matrix A ∈ R d × V .
Revised: = { x i 1 , x i 2 , x i 3 , ..., x in i } and u to a continuous space multiplying an embedding matrix A ∈ R d × V .

Original: Then, the  sentence x i can be represented to an object o i , using any of the following ﬁve methods: simple sum (equation 2), position encoding (J. Weston & Bordes, 2015) (equation 3), concatenation (equation 4), LSTM, and GRU.
Revised: Then, the embedded sentence is stored and represented as a memory object m i while question is represented as q . Any of the following methods are available for embedding component: simple sum (equation 1), position encoding (J. Weston & Bordes, 2015) (equation 2), concatenation (equation 3), LSTM, and GRU.

Original: In case of LSTM or GRU, o i is the ﬁnal hidden state of it.
Revised: In case of LSTM or GRU, m i is the ﬁnal hidden state of it.

Original: [Equation]
Revised: [Equation]

Original: As the following attention component takes the concatenation of o i and q , it is not necessarily  that story and questionto have the same dimensional embedding vectors unlike previous memoryaugmented neural networks.
Revised: As the following attention component takes the concatenation of m i and q , it is not necessarily the case that sentence and question have the same dimensional embedding vectors unlike previous memory-augmented neural networks.

Original: Attention component
Revised: 2. A TTENTION COMPONENT

Original: Attention component can be applied more than once depending on the problem; ﬁgure 2 illustrates 2 hop version of RMN.
Revised: Attention component can be applied more than once depending on the problem; Figure 1 illustrates 2 hop version of RMN.

Original: We refer to the i th embedded sentence on the t th hop as o ti .
Revised: We refer to the i th embedded sentence on the t th hop as m ti .

Original: Previously mentioned in section 3.1, we applied simple MLPs represented as g t θ s to constitute the attention component.
Revised: To constitute the attention component, we applied simple MLP represented as g tθ .

Original: g tθ must be ended with 1 unit output layer to provide a scalar weight w ti , which leads to an attention weight α ti .
Revised: It must be ended with 1 unit output layer to provide a scalar weight w ti , which leads to an attention weight α ti between 0 and 1.

Original: In the beginning, a vector concatenated with o 1 i and q ﬂows to the g 1 θ .
Revised: In the beginning, a vector concatenated with m 1 i and q ﬂows to the g 1 θ .

Original: From the result of g 1 θ , attention weight α 1 i is calculated using additional variable β 1  to control the intensity of attention, inspired by how Neural Turing Machine (Graves et al., 2014) reads from the memory.
Revised: From the result of g 1 θ , attention weight α 1 i is calculated using additional variable β 1 ( ≥ 1) to control the intensity of attention, inspired by the way Neural Turing Machine (Graves et al., 2014) reads from the memory.

Original: Then we get the related memory object r 1i , a weighted sum of α 1 i and  object o 1 i  .
Revised: Then we get the related memory object r 1 , a weighted sum of α 1 i and memory object m 1 i for all i .

Original: If there exist more hops, r 1i is  taken to the next hop and iterates over this process with the updated memory object o 2 i .
Revised: If there exist more hops, r 1 is directly taken to the next hop and iterates over this process with the updated memory object m 2 i .

Original: All the procedures are rewritten as equation  5,6, and 7:
Revised: All the procedures are rewritten as equation 4, 5, and 6:

Original: [Equation]
Revised: [Equation]

Original: Updating component
Revised: 2. U PDATING COMPONENT

Original: To forget the information alreadybeen used, we use  updating component to renew the memory.
Revised: To forget the information already used, we use intuitive updating component to renew the memory.

Original: It is replaced by the amount of unconsumed from the old one:
Revised: It is replaced by the amount of unconsumed from the old one:

Original: [Equation]
Revised: [Equation]

Original: [Equation]

Original: Contrary to other components, updating is not a mandatory component.
Revised: Contrary to other components, updating is not a mandatory component.

Original: When it is thought to have 1 hop, there is no need to use this.
Revised: When it is considered to have 1 hop, there is no need to use this.

Original: Reasoning component
Revised: 2. R EASONING COMPONENT

Original: Similar to attention component, reasoning component is also made up of MLP, represented as f φ .
Revised: Similar to attention component, reasoning component is also made up of MLP, represented as f φ .

Original: It receives both q and the ﬁnal result of attention component r f
Revised: It receives both q and the ﬁnal result of attention component r f

Original: and then take a softmax to produce the model answer ˆ a :
Revised: and then takes a softmax to produce the model answer ˆ a :

Original: [Equation]
Revised: [Equation]

Original:  E XPERIMENTS 4. D ATASET bAbI story-based QA dataset
Revised:  R ELATED W ORK 3. M EMORY -A UGMENTED N EURAL N ETWORK

Revised: To answer the question from a given set of facts, the model needs to memorize these facts from the past. Long short term memory (LSTM) (Hochreiter & Schmidhuber, 1997), one of the variants of recurrent neural network (RNN), is inept at remembering past stories because of their small internal memory (Sukhbaatar et al., 2015). To cope with this problem, J. Weston & Bordes (2015) has

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: [Equation]

Revised: [Table]

Revised: proposed a new class of memory-augmented model called Memory Network (MemNN).

Revised: MemNN comprises an external memory m and four components: input feature map ( I ), generalization ( G ), output feature map ( O ), and response ( R ).

Revised: I encodes the sentences which are stored in memory m .

Revised: G updates the memory, whereas O reads output feature o from the memory.

Revised: Finally, R infers an answer from o .

Revised: MemN2N, GMemN2N, DMN, and DMN+ all follow the same structure of MemNN from a broad perspective, however, output feature map is composed in slightly different way.

Revised: The relation between question and supporting sentences is realized from its cooperation.

Revised: MemN2N ﬁrst calculates the relatedness of sentences in the question and memory by taking the inner product, and the sentence with the highest relatedness is selected as the ﬁrst supporting sentence for the given question.

Revised: The ﬁrst supporting sentence is then added with the question and repeat the same operation with the updated memory to ﬁnd the second supporting sentence.

Revised: GMemN2N selects the supporting sentence in the same way as MemN2N, but uses the gate to selectively add the the question to control the inﬂuence of the question information in ﬁnding the supporting sentence in the next step.

Revised: DMN and

Revised: DMN + use output feature map based on various relatedness such as absolute difference, as well as inner product, to understand the relation between sentence and question at various points.

Revised: The more difﬁcult the task, the more complex the output feature map and the generalization component to get the correct answer.

Revised: For a dataset experimenting the text-based reasoning ability of the model, the overall accuracy could be increased in order of MemN2N, GMemN2N, DMN, and

Revised: DMN+, where the complexity of the component increases.

Revised: 3. R ELATION N ETWORK

Revised: Relation Network (RN) has emerged as a new and simpler framework for solving the general reasoning problem.

Revised: RN takes in a pair of objects as its input and simply learns from the compositions of two MLPs represented as g θ and f φ .

Revised: The role of each MLP is not clearly deﬁned in the original paper, but from the view of MemNN, it can be understood that g θ corresponds to O and f φ corresponds to R .

Revised: Table 1 summarizes the interpretation of RN compared to MemN2N and our model,

Revised: RMN.

Revised: To verify the role of g θ , we compare the output when pairs are made with supporting sentences and when made with unrelated sentences.

Revised: Figure 2 shows the visualization result of each output.

Revised: When we focus on whether the value is activated or not, we can see that g θ distinguishes supporting sentence pair from non-supporting sentence pair as output feature map examines how relevant the sentence is to the question.

Revised: Therefore, we can comprehend the output of g θ reveals the relation between the object pair and the question and f φ aggregates all these outputs to infer the answer.

Original:  bAbI story-based QA dataset (Weston et al., 2015) is composed of 20 different type of tasks for testing natural language reasoning ability.
Revised: [Figure]  E XPERIMENTS 4. D ATASET bAbI story-based QA dataset bAbI story-based QA dataset (Weston et al., 2015) is composed of 20 different types of tasks for testing natural language reasoning ability.

Original: Each task requires different methods to infer the answer.
Revised: Each task requires different methods to infer the answer.

Original: The dataset includes a set of statements, question and answer.
Revised: The dataset includes a set of statements comprised of multiple sentences, a question and answer.

Original: A statement can be as short as two sentences and as long as 320 sentences.
Revised: A statement can be as short as two sentences and as long assentences.

Original: To answer the question, it is necessary to ﬁnd relevant statements to a given question and derive answerword from them.
Revised: To answer the question, it is necessary to ﬁnd relevant one or more sentences to a given question and derive answer from them.

Original: Answer is typically a single word but in a few tasks, answers are a set of words.
Revised: Answer is typically a single word but in a few tasks, answers are a set of words.

Original: Each task is regarded as success when the accuracy is greater than 95%.
Revised: Each task is regarded as success when the accuracy is greater than 95%.

Original: There are two versions of this dataset, one that has 1k training examples and the other with 10k examples.
Revised: There are two versions of this dataset, one that has 1k training examples and the other with 10k examples.

Revised: Most of the previous models test their accuracy on 10k dataset with trained jointly.

Original: bAbI dialog dataset
Revised: bAbI dialog dataset

Original: bAbI dialog dataset (Bordes & Weston, 2016) is a set of 5 tasks within the goal-oriented context of restaurant reservation.
Revised: bAbI dialog dataset (Bordes & Weston, 2016) is a set of 5 tasks within the goal-oriented context of restaurant reservation.

Original: It is designed to test if model can learn various abilities such as performing dialog management, querying knowledge bases (KBs), and interpreting the output of such queries.
Revised: It is designed to test if model can learn various abilities such as performing dialog management, querying knowledge bases (KBs), and interpreting the output of such queries.

Original: The KB can be queried using API calls and 4 ﬁelds (a type of cuisine, a location, a price range, and a party size) should be ﬁlled to issue an API call.
Revised: The KB can be queried using API calls and 4 ﬁelds (a type of cuisine, a location, a price range, and a party size). They should be ﬁlled to issue an API call.

Original: Task 1 tests the capacity of interpreting a request and asking the right questions to issue an API call.
Revised: Task 1 tests the capacity of interpreting a request and asking the right questions to issue an API call.

Original: Task 2 checks the ability to modify an API call.
Revised: Taskchecks the ability to modify an API call.

Original: Task 3 and 4 tests the capacity of using outputs from an API call to propose options in the order of rating and to provide extra-information of what user asks for.
Revised: Task 3 and 4 test the capacity of using outputs from an API call to propose options in the order of rating and to provide extra-information of what user asks for.

Original: Task 5 combines everything.
Revised: Task 5 combines everything.

Revised: The maximum length of the dialog for each task is different: 14 for task 1, 20 for task 2, 78 for task 3, 13 for task 4, and 96 for task 5.

Original: As restaurant name, locations, and cuisine types always faceto new entities, there are plain and OOV test sets to assess model’s generalization ability.
Revised: As restaurant name, locations, and cuisine types always face new entities, there are normal and OOV test sets to assess model’s generalization ability.

Original: Training sets are 1k examples, which is small, to create realistic learning conditions.
Revised: Training sets consist fo 1k examples, which is not a large amount of creating realistic learning conditions.

Original: 4. T RAINING DETAILS bAbI story-based QA dataset
Revised: 4. T RAINING DETAILS bAbI story-based QA dataset

Original: We trained 2 hop RMN jointly on all tasks using 10k dataset for model to infer the solution suited to each type of tasks.
Revised: We trained 2 hop RMN jointly on all tasks using 10k dataset for model to infer the solution suited to each type of tasks.

Original: We limited the input to the last 70 stories for all tasks except task 3 for which we limited input to the last 130 stories, similar to Sukhbaatar et al.
Revised: We limited the input to the last 70 stories for all tasks except task 3 for which we limited input to the last 130 stories, similar to Xiong et al.

Original: , Liu & Perez (2017), and Xiong et al. (2016).
Revised: (2016) which is the hardest condition among previous models.

Original: Then, we labeled each sentence with its relative position.
Revised: Then, we labeled each sentence with its relative position.

Original: Embedding component is similar to Santoro et al.
Revised: Embedding component is similar to Santoro et al.

Original: (2017), where story and question are embedded through different LSTMs; 32 unit word-lookup embeddings; 32 unit LSTM for story and question.
Revised: (2017), where story and question are embedded through different LSTMs; 32 unit word-lookup embeddings; 32 unit LSTM for story and question.

Original: For attention component, as we use 2 hop RMN, there are g 1 θ
Revised: For attention component, as we use 2 hop RMN, there are g 1 θ

Original: and g 2 θ ; both are threelayer MLP consisting of 256, 128, 1 unit with ReLU activation function (Nair & Hinton, 2010).
Revised: and g 2 θ ; both are three-layer MLP consisting of 256, 128, 1 unit with ReLU activation function (Nair & Hinton, 2010).

Original: f φ is composed of 512, 512, and 159 units (the number of words appeared in bAbI dataset is 159) of three-layer MLP with ReLU non-linearities where the ﬁnal layer was a linear that produced logits for a softmax over the answer vocabulary.
Revised: f φ is composed of 512, 512, and 159 units (the number of words appearing in bAbI dataset is 159) of three-layer MLP with ReLU non-linearities where the ﬁnal layer was a linear that produced logits for a softmax over the answer vocabulary.

Original: For regularization, we use batch normalization (Ioffe &
Revised: For regularization, we use batch normalization (Ioffe &

Original: Szegedy, 2015) for all MLPs.
Revised: Szegedy, 2015) for all MLPs.

Original: The softmax output was optimized with a cross-entropy loss function using the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 2 e − 4 .
Revised: The softmax output was optimized with a cross-entropy loss function using the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 2 e − 4 .

Original: bAbI dialog dataset
Revised: bAbI dialog dataset

Original: We trained on full dialog scripts with every model response as answer, all previous dialog history as stories, and the last user utterance as question.
Revised: We trained on full dialog scripts with every model response as answer, all previous dialog history as sentences to be memorized, and the last user utterance as question.

Original: Model selects the most probable response from 4,212 candidates which are ranked from a set of all bot utterances appearing in training, validation and test sets (plain and OOV) for all tasks combined.
Revised: Model selects the most probable response from 4,212 candidates which are ranked from a set of all bot utterances appearing in training, validation and test sets (plain and OOV) for all tasks combined.

Original: We also report results when we use match type features for dialog.
Revised: We also report results when we use match type features for dialog.

Original: Match type feature is an additional label on the candidates indicating if word is found on the dialog history.
Revised: Match type feature is an additional label on the candidates indicating if word is found on the dialog history.

Revised: For example, if the world ‘Seoul’ is found, then the ‘location’ ﬁeld is checked to hint model this word is important and should be used in API call.

Original: This feature can alleviate OOV problem.
Revised: This feature can alleviate OOV problem.

Original: Training was done with Adam optimizer and a learning rate of 1 e − 4 for all tasks.
Revised: Training was done with Adam optimizer and a learning rate of 1 e − 4 for all tasks.

Original: Additional model details are given in Appendix A. 
Revised: Additional model details are given in Appendix A. [Table]

Original: 4. R ESULTS AND D ISCUSSION 4.3. T IME COMPLEXITY
Revised: [Table] [Table] [Figure]

Original: As attention component g θ is applied to each object in memory referred in equation 5, the complexity reduces to O ( n ) . We compared processing time of a unit batch size 256 and the result is shown in ﬁgure 3. Because of the GPU memory exploding issue, there is no result for more than 170 sentences.
Revised: [Table]

Original:  4.3. B A B I STORY - BASED QA
Revised:  R ESULTS AND D ISCUSSION 5. B A B I STORY - BASED QA

Original: As we can see in table 1, RMN shows state-of-the-art result on bAbI story-based Question Answering dataset: 98.8% accuracy where only a single task with no catastrophic failure .
Revised: As we can see in table 2, RMN shows state-of-the-art result on bAbI story-based Question Answering dataset: 98.8% accuracy where only a single task with no catastrophic failure. It succeeded on task 17, 18, 19 where MemN2N, GMemN2N, and DMN are failed to solve, and on task 16 which DMN+, DNC, and EntNet scored high error rates.

Original: Table 2 shows how our model solved several tasks.
Revised: Table 3 shows how our model solved several tasks.

Original: RMN’s attention component g 1 θ
Revised: RMN’s attention component g 1 θ

Original: and g 2 θ complement each other to identify the necessary facts to answer correctly.
Revised: and g 2 θ complement each other to identify the necessary facts to answer correctly.

Original: Sometimes both g 1 θ and g 2 θ concentrate on the same stories which are all critical to answer the question,  sometimes g 1 θ ﬁnds a fact related to the given question and with this information g 2 θ chooses the key fact to answer kitchen.
Revised: Sometimes both g 1 θ and g 2 θ concentrate on the same sentences which are all critical to answer the question, and sometimes g 1 θ ﬁnds a fact related to the given question and with this information g 2 θ chooses the key fact to answer.

Original: While trained jointly, RMN learns these different solution for different task.
Revised: While trained jointly, RMN learns these different solutions for each task.

Original: For the task 3,[Table] the only failed task, attention component still functions well;  focuses sequentially on the supporting sentences.
Revised: For the task 3, the only failed task, attention component still functions well; it focuses sequentially on the supporting sentences.

Original: However, the reasoning component, f φ , hada difﬁcultyon catching a word ‘before’.
Revised: However, the reasoning component, f φ , had difﬁculty catching the word ‘before’.

Original: We could easily ﬁgure out ‘before’ implies ‘just before’ the certain situation, whereas RMN confused its meaning.
Revised: We could easily ﬁgure out ‘before’ implies ‘just before’ the certain situation, whereas RMN confused its meaning.

Original: As shown in table 2c, our model found all previous locations before the garden.
Revised: As shown in table 3c, our model found all previous locations before the garden.

Original: Still, it is remarkable that the simple MLP carried out all of these various roles.
Revised: Still, it is remarkable that the simple MLP carried out all of these various roles.

Original: 4.3. B A B I DIALOG
Revised: 5. B A B I DIALOG

Original: Results are given in  Table 3.
Revised: [Table] The results in the Table 4 show that the RMN has the best results in any conditions.

Original:  RMN outperforms previous memory-augmented models with a signiﬁcant difference without given any match type.
Revised: Without any match type, RN and RMN outperform previous memory-augmented models on both normal and

Original: Unlike previous models, match type features are helpful only on OOV tasks. This is mainly attributed to the impressive result on task 4.
Revised: OOV tasks. This is mainly attributed to the impressive result on task 4 which can be interpreted as an effect of MLP based output feature map.

Original: Table 4c shows how RMN solved task 4.
Revised: To solve task 4, it is critical to understand the relation between ‘phone number’ of user input and ‘r phone’ of previous dialog as shown in Table 8c.

Original: The attention component adequately focused on necessary knowledge base and the reasoning component inferred appropriate answer even on the OOV task.
Revised: We assumed that inner product was not sufﬁcient to capture their implicit similarity and performed an supporting experiment. We converted RMN’s attention component to inner product based attention, and the results revealed the error rate increased to 11.3%.

Original: It implies f φ is able to leverage information coming from the entities whether they are seen or unseen in the training dialog.
Revised: For the task 3 and task 5 where the maximum length is especially longer than the others, RN performs worse than MemN2N, GMemN2N and RMN. The number of unnecessary object pairs created by the RN not only increases the processing time but also decreases the accuracy. With the match type feature, all models other than RMN have signiﬁcantly improved their performance except for task 3 compared to the plain condition. RMN was helped by the match type only on the OOV tasks and this implies RMN is able to ﬁnd relation in the With Match condition for the normal tasks.

Original: On the other hand, f φ failed to perform well on the OOV task 1 and 2 even though g 1 θ properly focused on the related object.
Revised: When we look at the OOV tasks more precisely, RMN failed to perform well on the OOV taskand 2 even though g 1 θ properly focused on the related object as shown in Table 8a.

Original: We state that thiscontrast is originated from the information contained in one sentence is relatively large in task 1 and 2.
Revised: We state that this originated from the fact that the number of keywords in task 1 and 2 is bigger than that in task 4.

Revised: In task 1 and 2, all four keywords (cuisine, location, number and price) must be correctly aligned from the supporting sentence in order to make the correct API call which is harder than task 4.

Original: Consider the example in Table 4a and Table 4c.
Revised: Consider the example in Table 8a and Table 8c.

Original: Supporting sentence of task 4 have one keyword out of three words, whereas supporting sentences of task 1 and 2 consist of four keywords (cuisine, location, number and price) out of sixteen words.
Revised: Supporting sentence of task 4 have one keyword out of three words, whereas supporting sentences of task 1 and 2 consist of four keywords (cuisine, location, number and price) out of sixteen words.

Original: This confuses RMN to recognize critical information to choose a right answer. [Table] [Table]
Revised: Different from other tasks, RMN yields the same error rate 25.1% with MemN2N and GMemN2N on the task 3.

Original: The main goal of task 3 is to recommend restaurant from knowledge base in the order of rating.
Revised: The main goal of task 3 is to recommend restaurant from knowledge base in the order of rating.

Original: Different from other tasks, RMN yields same error rate 25.1% with MemN2N and GMemN2N.

Original: All failed cases are displaying restaurant where the user input is < silence > which is somewhat  ambiguous trigger to ﬁnd question relevant information.
Revised: All failed cases are displaying restaurant where the user input is < silence > which is somewhat an ambiguous trigger to ﬁnd the input relevant previous utterance.

Original: As shown in Table 4b, there are two different types of response to the same user input.
Revised: As shown in Table 8b, there are two different types of response to the same user input.

Original: One is to check whether all the required ﬁelds are given from the previous dialog and then ask user for the missing ﬁelds or send a “Ok let me look into some options for you.”
Revised: One is to check whether all the required ﬁelds are given from the previous utterances and then ask user for the missing ﬁelds or send a “Ok let me look into some options for you.”

Original: message.
Revised: message.

Original: The other type is to recommend restaurant starts from the highest rating.
Revised: The other type is to recommend restaurant starting from the highest rating.

Original: Three models show lack of ability to discriminate these two types  so that concluded to the same results.
Revised: All models show lack of ability to discriminate these two types of silences so that concluded to the same results.

Original: To verify our statement
Revised: To verify our statement, we performed an additional experiment on task 3 and checked the performance gain (extra result is given in Tableof Appendix B). 5. M ODEL ANALYSIS Effectiveness of the MLP-based output feature map The most important feature that distinguishes MemNN based models is the output feature map. Table 5 summarizes the experimental results for the bAbI story-based QA dataset when replacing the RMN’s MLP-based output feature map with the idea of the previous models.

Original:  [Table]
Revised: inner product was used in MemN2N, inner product with gate was used in GMemN2N, and inner product and absolute difference with two embedding matrices was used in DMN and DMN+. From the Table 5, the more complex the output feature map, the better the overall performance. In this point of view, MLP is the effective output feature map. [Table]

Revised: Performance of RN and RMN according to memory size Additional experiments were conducted with the bAbI story-based QA dataset to see how memory size affects both performance and training time of RN and RMN.

Revised: Test errors with training time written in parentheses are summarized in Table 6.

Revised: When memory size is small, we could observe the data-effeciency of RN.

Revised: It shows similar performance to RMN in less time.

Revised: However, when the memory size increases, performance is signiﬁcantly reduced compared to RMN, even though it has been learned for a longer time.

Revised: It is even lower than itself when the memory size is 20.

Original: we performed additional experiment on task 3 and check the  performance gain (extra result is given in Table 6 of Appendix B).
Revised: On the other hand, RMN maintains high performance even when the memory size increases. [Table] Effectiveness of the number of hops bAbI story based QA dataset differs in the number of supporting sentences by each task that need to be referenced to solve problems. For example, task 1, 2, and 3 require single, two, and three supporting facts, respectively.

Revised: The result of the mean error rate for each task according to the number of hops is in Table 7.

Revised: Overall, the number of hops is correlated with the number of supporting sentences.

Revised: In this respect, when the number of relations increases, RMN could reason across increasing the number of hops to 3, 4 or more.

Revised: [Table]

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: Our work, RMN, is a simple and powerful architecturefor relational reasoning that restructured RN module as 4 components of Memory Network.
Revised: Our work, RMN, is a simple and powerful architecture that effectively handles text-based question answering tasks when large size of memory and high reasoning ability is required.

Original: We found the similarity between the role of output feature map of Memory Network toread relevant object given the input object and g θ of RN to infer if two objects are related.
Revised: Multiple access to the external memory to ﬁnd out necessary information through a multi-hop approach is similar to most existing approaches.

Original: From this interesting discovery, we modiﬁed g θ as an attention component and raised the state-of-the-art performance on  story-based QA and goal-oriented dialog dataset.
Revised: However, by using a MLP that can effectively deal with complex relatedness when searching for the right supporting sentences among a lot of sentences, RMN raised the state-of-the-art performance on the story-based QA and goal-oriented dialog dataset.

Original: It is A key contribution of RMN is it reduces RN’s problematic quadratic complexity to linear complexity which enables applications on broader domains while preserving RN’s simplicity .
Revised: When comparing RN which also used MLP to understand relations, RMN was more effective in the case of large memory.

Original: Future work will apply RMN to image based reasoning task (e.g., CLEVR).
Revised: Future work will apply RMN to image based reasoning task (e.g., CLEVR, DAQUAR, VQA etc.).

Original: To extract features from the image, VGG net (Simonyan & Zisserman, 2014) is used in convention and outputs 196 objects of 512 dimensional vector which also requires large sized memory.
Revised: To extract features from the image, VGG net (Simonyan & Zisserman, 2014) is used in convention and outputs 196 objects of 512 dimensional vectors which also require large sized memory.

Original: An important direction will be to ﬁnd an appropriate way to focus sequentially on relatedimage object which was rather easy in text based reasoning.
Revised: An important direction will be to ﬁnd an appropriate way to focus sequentially on related object which was rather easy in text-based reasoning.

Original: [Table]
Revised: [Table]

Original: [Table]

Original: [Table] and [Table] result of 
Revised: (c) Plain and OOV result of task

Original: [Table]

Original: [Table]
Revised: [Table]

Original: A M ODEL DETAILS
Revised: A M ODEL DETAILS

Original: [Table]
Revised: [Table]

Original: B A DDITIONAL R ESULTS
Revised: B A DDITIONAL R ESULTS

Original: [Table]
Revised: [Table]

Original: [Table]

Original: [Table]

Original: for two with
Revised: for two with

Original: [Table]
Revised: [Table]

Original: on
Revised: on

Original: [Table]
Revised: [Table]

Original: Where should it be
Revised: Where should it be

Original: [Table]
Revised: [Table]

Original: Which
Revised: Which

Original: [Table]
Revised: [Table]

Original: are looking for
Revised: are looking for

Original: [Table]
Revised: [Table]

Original: We modify the user input from < silence > to < silence >< silence > when looking for restaurant recommendations.
Revised: We modify the user input from < silence > to < silence >< silence > when looking for restaurant recommendations.

Original: This makes model to distinguish two different situations whether to ask for additional ﬁelds or to recommend restaurant.
Revised: This makes model to distinguish two different situations whether to ask for additional ﬁelds or to recommend restaurant.