Original: Commonsense Reasoning for Question Answering with Explanations
Revised: Commonsense Reasoning for Question Answering with Explanations

Original: Anonymous ACL submission

Original: Abstract
Revised: Abstract

Original: Commonsense reasoning is an important capability for a range of AI applications such astext understanding.
Revised: Commonsense reasoning is an important capability for a range of AI applications such as text understanding.

Original: Neural models for commonsense reasoning QA often directly predictanswers based on learned representations oflanguage.
Revised: Neural models for commonsense reasoning QA often directly predict answers based on learned representations of language.

Original: In this work, we consider the challenge of producing an explicit reasoning stepfor a commonsense QA system.
Revised: In this work, we consider the challenge of producing an explicit reasoning step for a commonsense QA system.

Original: We propose alatent-variable model that identifies what typeof knowledge from an external knowledge basemay be relevant to answering the question, computes the commonsense inferences, and predicts the answer.
Revised: We propose a latent-variable model that identifies what type of knowledge from an external knowledge base may be relevant to answering the question, computes the commonsense inferences, and predicts the answer.

Original: Our method can thereforelearn to provide posterior rationales for whya certain answer was chosen.
Revised: Our method can therefore learn to provide posterior rationales for why a certain answer was chosen.

Original: Experimentalresults show that the model can identify thecorrect reasoning step in twice as many examples compared to an existing unsupervised approach for producing explanations, while stillmaintaining comparable accuracy to end-to-endpretrained models.
Revised: Experimental results show that the model can identify the correct reasoning step in twice as many examples compared to an existing unsupervised approach for producing explanations, while still maintaining comparable accuracy to end-to-end pretrained models.

Original:  Introduction
Revised:  Introduction

Original: Commonsense is knowledge that is consideredobvious to most humans.
Revised: Commonsense is knowledge that is considered obvious to most humans.

Original: Commonsense reasoning uses this knowledge to solve complex reasoning tasks (Sap et al., 2020; Cambria et al., 2010).
Revised: Commonsense reasoning uses this knowledge to solve complex reasoning tasks (Sap et al., 2020; Cambria et al., 2010).

Original: Specifically, we study multiple-choice QA (MCQ)that requires commonsense reasoning.
Revised: Specifically, we study multiple-choice QA (MCQ) that requires commonsense reasoning.

Original: Recent approaches have applied end-to-end pretrained language models (PLMs) to solve MCQ.
Revised: Recent approaches have applied end-to-end pretrained language models (PLMs) to solve MCQ.

Original: A downsideof the approaches is that it is impossible to extractthe explicit reasoning steps used by the model.
Revised: A downside of the approaches is that it is impossible to extract the explicit reasoning steps used by the model.

Original: Toget around this issue, Bansal et al.
Revised: To get around this issue, Bansal et al.

Original: ; Paranjape et al.
Revised: ; Paranjape et al.

Original: (2021) proposed to directly predict intermediate steps in the reasoning chain.
Revised: (2021) proposed to directly predict intermediate steps in the reasoning chain.

Original: However,these methods require direct supervision on the reasoning steps, which implies manual annotations.
Revised: However, these methods require direct supervision on the reasoning steps, which implies manual annotations.

Original: Bosselut et al.
Revised: Bosselut et al.

Original: (2021) developed an unsupervisedapproach to obtain explanations by leveraging adynamic knowledge base (KB).
Revised: (2021) developed an unsupervised approach to obtain explanations by leveraging a dynamic knowledge base (KB).

Original: However, becausethis approach does not involved any learning component with respect to the target task, its ability to identify reasoning steps is limited.
Revised: However, because this approach does not involved any learning component with respect to the target task, its ability to identify reasoning steps is limited.

Original: In this work, we consider the problem of learning the reasoning path for MCQ that requires commonsense reasoning, without sacrificing the benefits of pretrained neural models.
Revised: In this work, we consider the problem of learning the reasoning path for MCQ that requires commonsense reasoning, without sacrificing the benefits of pretrained neural models.

Original: Explicitly, we propose a structured latent-variable approach that can learn the intermediate reasoning step for answering a question without supervision.
Revised: Explicitly, we propose a structured latent-variable approach that can learn the intermediate reasoning step for answering a question without supervision.

Original: Our model first identifies what type of knowledge from an external KB may be relevant, then obtains that knowledge from the KB; finally, the model predicts an answer.
Revised: Our model first identifies what type of knowledge from an external KB may be relevant, then obtains that knowledge from the KB; finally, the model predicts an answer.

Original: In Table 1, we present an example of the reasoning step our model has inferred. We empirically evaluate our method on the socialIQA dataset (Sap et al., 2019) and show that we are able to achieve similar accuracy to that of a pretrained model while we identify the explanations.
Revised: We empirically evaluate our method on the socialIQA dataset (Sap et al., 2019) and show that we are able to achieve similar accuracy to that of a pretrained model while we identify the explanations.

Original: We also introduce a new evaluation set that annotates the correct reasoning steps drawn from comet2020 (Hwang et al., 2021) for test examples in socialIQA (Sap et al., 2019).
Revised: We also introduce a new evaluation set that annotates the correct reasoning steps drawn from comet2020 (Hwang et al., 2021) for test examples in socialIQA (Sap et al., 2019).

Original: On this new evaluation set, we analyze the generated explanations and show our model is able to find the correct reasoning steps in 45% cases compared to 22% for the dynamic KB method.
Revised: On this new evaluation set, we analyze the generated explanations and show our model is able to find the correct reasoning steps in 45% cases compared to 22% for the dynamic KB method.

Original:  Related Work
Revised:  Related Work

Original: Learning explanations for commonsense reasoning.
Revised: Learning explanations for commonsense reasoning.

Original: Several multi-stage models have been proposed to produce explanations for commonsense
Revised: Several multi-stage models have been proposed to produce explanations for commonsense

Original: MCQ problems.
Revised: MCQ problems.

Original: Bansal et al.
Revised: Bansal et al.

Original: (2021) first trained a model to infer free-form commonsense from the context; then they used a separate model to predict the answer conditioned on both the context and the commonsense.
Revised: (2021) first trained a model to infer free-form commonsense from the context; then they used a separate model to predict the answer conditioned on both the context and the commonsense.

Original: Paranjape et al.
Revised: Paranjape et al.

Original: (2021) learned to generate contrastive commonsense explanations for coreference resolution.
Revised: (2021) learned to generate contrastive commonsense explanations for coreference resolution.

Original: We note that both methods are supervised and require manually provided explanations.
Revised: We note that both methods are supervised and require manually provided explanations.

Original: Additionally, Shwartz et al.
Revised: Additionally, Shwartz et al.

Original: [Table] hand-crafted a number of commonsense knowl- [Table] edge templates, and the templates were later filledby pretrained models, which could be viewed as anexplanation for choosing an answer.
Revised: (2020) hand-crafted a number of commonsense knowledge templates, and the templates were later filled by pretrained models, which could be viewed as an explanation for choosing an answer.

Original: Finally, Lin et al.
Revised: Finally, Lin et al.

Original: (2017) developed a similargenerative approach for a machine reading comprehension problem.
Revised: (2017) developed a similar generative approach for a machine reading comprehension problem.

Original: They mined heterogeneousknowledge from different sources and identified thereasoning trajectory by using an attention mechanism over the mined knowledge.
Revised: They mined heterogeneous knowledge from different sources and identified the reasoning trajectory by using an attention mechanism over the mined knowledge.

Original: However, weassume different data generating processes and employ different mechanisms to incorporate KBs.
Revised: However, we assume different data generating processes and employ different mechanisms to incorporate KBs.

Original: Incorporating external knowledge sources.
Revised: Incorporating external knowledge sources.

Original: Because leveraging external knowledge sources is akey component in generating explanations in ourapproach, we also provide an overview of different ways to incorporate such sources into QA systems.
Revised: Because leveraging external knowledge sources is a key component in generating explanations in our approach, we also provide an overview of different ways to incorporate such sources into QA systems.

Original: Bauer et al.
Revised: Bauer et al.

Original: Lin et al.
Revised: Lin et al.

Original: Fenget al.
Revised: ; Feng et al.

Original: ; Paul and Frank (2019); Yasunagaet al.
Revised: ; Paul and Frank (2019); Yasunaga et al.

Original: ; Wang et al.
Revised: ; Wang et al.

Original: (2020) extracted entitiesin contexts/questions/answers and built knowledgegraphs on the entities according to external KBs;some of them made specific network architecturechanges to include this information.
Revised: (2020) extracted entities in contexts/questions/answers and built knowledge graphs on the entities according to external KBs; some of them made specific network architecture changes to include this information.

Original: Bauer and
Revised: Bauer and

Original: Bansal (2021) developed a method to choose the
Revised: Bansal (2021) developed a method to choose the

Original: KB that has knowledge most aligned with the targettask given several candidate KBs.
Revised: KB that has knowledge most aligned with the target task given several candidate KBs.

Original: Xia et al.
Revised: Xia et al.

Original: (2019)performed multi-task learning, where, in additionto the original task, they added hand-crafted auxiliary tasks based on nodes and edges in the KBto improve generalization.
Revised: (2019) performed multi-task learning, where, in addition to the original task, they added hand-crafted auxiliary tasks based on nodes and edges in the KB to improve generalization.

Original: The aforementionedapproaches all use static KBs, which only havefixed nodes.
Revised: The aforementioned approaches all use static KBs, which only have fixed nodes.

Original: Bosselut et al.
Revised: Bosselut et al.

Original: (2021) instead utilizeda dynamic KB to retrieve knowledge relevant tothe context.
Revised: (2021) instead utilized a dynamic KB to retrieve knowledge relevant to the context.

Original: Due to its strong generalization capacity, we use the method and adapt it to generatecommonsense inferences.
Revised: Due to its strong generalization capacity, we use the method and adapt it to generate commonsense inferences.

Original:  Method
Revised:  Method

Original:  The goal of this work is to generate explanations for MCQ that requires commonsense reasoning.
Revised: [Figure] The goal of this work is to generate explanations for MCQ that requires commonsense reasoning.

Original: Whenc q r o a humans do MCQ, they often read the choices first and quickly come up with an answer. Then, they work backwards to figure out what commonsense knowledge they have used to reach the answer.
Revised: We consider how humans generate explanations from the psychological point of view: given an event that a person believes has happened, the person come up with a set of explanations and select the one that best explains the event (Gordon and Hobbs, 2017).

Original: For example, there is a question, “Jordan took a football outside to play with their friends.
Revised: For example, we know that “Jack needed some money, so he went and shook his piggy bank.

Original: What does
Revised: He was disappointed when it made no sound. " When we are asked to explain the reason for the pippy bank made no sound, and the two possible commonsense explanations are “the piggy bank’s physical material makes no sound” and “there is not money in the pippy bank.”

Original: Jordan need to do before this?”
Revised: Because the latter better explains the event in that making no sound disappointed Jack, we believe this explanation is correct.

Original: There are many things one needs to do before the event, such as finding a playground, gathering the friends, etc., but the correct choice is“buy a football.” Due to this ambiguity, humanstend to perform explicit reasoning afterwards.
Revised: We follow this procedure to produce explanations for humans to perform evaluation: we first let humans believe the MCQ example (with the answer produced by the model) is a past event, and ask humans to choose an explanation that best explains the event.

Original: Therefore, we consider the explanation to come after an answer being chosen.
Revised: Therefore, we consider the posterior rationales to be the explanation.

Revised: The existing literature also refers to the process of generating commonsense explanations for given events as abductive commonsense reasoning (Bhagavatula et al., 2019).

Original: Our approach for computing  explanations will be to utilize a generative model that first retrieves knowledge relevant for a given context from an external KB.
Revised: Our approach for computing the posterior explanations will be to utilize a generative model that first retrieves knowledge relevant for a given context from an external KB.

Original: In particular, we will use Resource Description Framework (RDF) triples (Auer et al., 2007; Bollacker et al., 2008) to represent commonsense knowledge.
Revised: In particular, we will use Resource Description Framework (RDF) triples (Auer et al., 2007; Bollacker et al., 2008) to represent commonsense knowledge.

Original: For a given MCQ example, we can then utilize this generative model to infer the explicit reasoning used by the system on specific commonsense examples.
Revised: For a given MCQ example, we can then utilize this generative model to infer the explicit reasoning used by the system on specific commonsense examples.

Original: At a high level, we assume there is unobserved commonsense knowledge that is necessary for reaching the correct answer.
Revised: At a high level, we assume there is unobserved commonsense knowledge that is necessary for reaching the correct answer.

Original: However, there is a large number of commonsense tuples that may be relevant, so we need to identify the specific one that is required given the context and the question 1 .
Revised: However, there is a large number of commonsense tuples that may be relevant, so we need to identify the specific one that is required given the context and the question 1 .

Original: Therefore, the goal of our model is to find this missing piece and return it as the explanation for thecorrect answer.
Revised: Therefore, the goal of our model is to find this missing reasoning step and return it as the explanation for the correct answer.

Revised: We walk readers through the generative process with examples shown in Table 1.

Revised: End-to-end neural models directly produce an answer distribution based on contexts and questions; however, we generate a reasoning step from con-

Revised: [Table]

Original: Formally, MCQ problems start with a contextc and question q .
Revised: Question Reasoning step (r, o) Answers Carson brought the spoon to Taylor’s mouth so Taylor could eat.

Revised: Desires to be helpful

Revised: HasSubevent PersonY is eating a) bring a cup

Revised: MotivatedByGoal they are full b) leave the house

Revised: What does Carson need to do before this?

Revised: xEffect gets thanked c) sit with Taylor ➨ xNeed to be near someone

Revised: Lee found the Northeast to be way too cold.

Revised: Lee decided to move to Florida.

Revised: ➨ Desires to get away from the cold

Revised: HasSubevent get a new job a) happy

Revised: MotivatedByGoal cold b) likes cold weather

Revised: How would you describe Lee?

Revised: xEffect gets cold c) likes the heat xNeed to have a job

Revised: Riley wanted to get a raise and started to work very hard at work.

Revised: Desires to be successful

Revised: HasSubevent get a raise a) work hard as well

Revised: MotivatedByGoal they get a raise b) quit work

Revised: What will Others want to do next?

Revised: ➨ xEffect gets promoted c) be alone xNeed to be a good employee

Revised: Tracy sat down next to Ash and began softly kissing him.

Revised: ➨ Desires to show affection

Revised: HasSubevent get closer a) be romantic

Revised: MotivatedByGoal none b) kiss back

Revised: What will Ash want to do next?

Revised: xEffect gets kissed back c) run away xNeed to be near him texts and produce the the answer distribution also based on this intermediate step.

Revised: In the first example of Table 1, the reasoning step the generative model produces is that Carson needs to be near someone in order to make the event in the context happen, which helps to reach the answer.

Revised: More specifically, the generative model produces this reasoning step in two stages: the first stage identifies the relation (in blue) that may be relevant for the context and the question, and the second stage generates the most plausible object (in brown).

Revised: Note that compared to prior work like (Bosselut et al., 2021), the prior work only produces commonsense inferences as intermediate step, but our model also selects the best explanation among all other explanations based on the learned representation of the context and the question.

Revised: Formally, MCQ problems start with a context c and question q .

Original: The goal is to produce a distribution over answer strings a , defined by P ( a | c, q ) .
Revised: The goal is to produce a distribution over answer strings a , defined by P ( a | c, q ) .

Original: To model this distribution we will introduce a latent explanation in the form of a partialRDF triplez =
Revised: To model this distribution we will introduce a latent explanation in the form of a partial

Original:  ( r, o ) , such that (cid:80) z P ( a, z | c, q ) .
Revised: RDF triple z = ( r, o ) , such that (cid:80) z P ( a, z | c, q ) .

Original: The complete RDF triplet has form ( s, r, o ) where s, o ∈ V ∗are a subject and an object, respectively, and r ∈ Ris the relation between s and o .
Revised: The complete RDF triplet has form ( s, r, o ) where s, o ∈ V ∗ are a subject and an object, respectively, and r ∈ R is the relation between s and o .

Original: V is a vocabulary,and R is a fixed set.
Revised: V is a vocabulary, and R is a fixed set.

Original: o is a commonsense inferencethat is inferred given s and r . In the MCQ task, scan be deterministically computed from the contextc , whereas r and o cannot. We use the same way asthat in Bosselut et al. (2021) to compute s from c .
Revised: In the MCQ task, we set s to be c , as c can be viewed as an event, and one can make commonsense inference from the event with different relations.

Original: Figure 1 shows the generative process, whichproceeds in three stages:[Equation] [Equation] [Equation]
Revised: Figure 1 shows the generative process, which proceeds in three stages:

Original: ∼

Original: [Equation]
Revised: [Equation]

Original: Answer Model

Original: The following three sections describe each of thesestages in more detail.
Revised: The following three sections describe each of these stages in more detail.

Original: 3. Relation Model
Revised: 3. Relation Model

Original: When generating a reasoning step, the relationmodel determines what type of commonsense fromthe external KB is required to answering the question under the context.
Revised: When generating a reasoning step, the relation model determines what type of commonsense from the external KB is required to answering the question under the context.

Original: For example, when one hasa question asking “how would you describe X," thecommonsense usually come up in one’s mind is X’s physical entities if X is an object or X’s characteristics if X is a person.
Revised: For example, when one has a question asking “how would you describe X," the commonsense that usually come up in one’s mind is X’s physical attributes if X is an object or X’s characteristics if X is a person.

Original: Including the contextfurther reduces ambiguity because without a scope,a question could have many interpretations.
Revised: Including the context further reduces ambiguity because without a scope, a question could have many interpretations.

Original: Therefore, the relation model specifies a distribution overall relations conditioned on c and q .
Revised: Therefore, the relation model specifies a distribution over all relations conditioned on c and q .

Original:  P ( r | c, q ; θ )isparameterized by a BERT model with a multiplechoice head (Kenton and Toutanova, 2019) whichtakes [ CLS ] c [ SEP ] q [ SEP ] r as input.
Revised: We parameterize P ( r | c, q ; θ ) by a BERT model which takes in [ CLS ] c [ SEP ] q [ SEP ] r . r is the relation symbol from the relation set defined by the external knowledge base (and this also applies to the later models where r is also part of the input).

Original: 3. Object Model
Revised: 3. Object Model

Original: After the type of relevant knowledge is identified,the object model then generates commonsense in- ferences given the context and the relation.
Revised: After the type of relevant knowledge is identified, the object model then generates commonsense inferences given the context and the relation.

Original: Learn- ing to infer the commonsense knowledge from a context and a knowledge type is in fact a wellstudied problem, called KB completion (Saito et al., 2018; Malaviya et al., 2020).
Revised: Learning to infer the commonsense knowledge from a context and a knowledge type is in fact a wellstudied problem, called KB completion (Saito et al., 2018; Malaviya et al., 2020).

Original: We thus treat it as a
Revised: We thus treat it as a

Original: KB completion task.
Revised: KB completion task.

Original: The object model specifies a distribution over all objects o conditioned on c and r .
Revised: The object model specifies a distribution over all objects o conditioned on c and r .

Original: P ( o | c, r ; ϕ ) is parameterized by a BART model (Lewis et al., 2020), where the input is a concatenation of c and r .
Revised: P ( o | c, r ; ϕ ) is parameterized by a BART model (Lewis et al., 2020), where the input is a concatenation of c and r .

Original: 3. Answer Model
Revised: 3. Answer Model

Original: We arrive at the final component of our generative model, which governs how the information about contexts, questions, and knowledge are rendered into answers.
Revised: We arrive at the final component of our generative model, which governs how the information about contexts, questions, and knowledge are rendered into answers.

Original: The answer model explicitly considers the commonsense inferred from the context by conditioning on the RDF triplet.
Revised: The answer model explicitly considers the commonsense inferred from the context by conditioning on the RDF triplet and produces a probability distribution over all answers.

Original: P ( a | c, r, o, q ; ψ ) is also parameterized by a BERT model with a multiple-choice head.
Revised: P ( a | c, r, o, q ; ψ ) is also parameterized by a BERT model with a multiple-choice head.

Original: The input to the model is [ CLS [Equation]
Revised: The input to the model is [ CLS ] c [ SEP ] r [ SEP ] o

Original:  SEP [Equation]
Revised: [ SEP ] q [ SEP ] a .

Original: 3. Training and Inference
Revised: 3. Training and Inference

Original: The generative model is trained in two steps.
Revised: The generative model is trained in two steps.

Original: It first learns the object model; then, it uses the following objective to jointly learn the relation model and the answer model, summing out r, o :
Revised: It first learns the object model; then, it uses the following objective to jointly learn the relation model and the answer model, summing out r, o :

Original: [Equation]
Revised: [Equation]

Original: r | c, q ; θ ) .

Original: Because V ∗ is a combinatorially large space, exactly enumerating all objects is intractable.
Revised: Because V ∗ is a combinatorially large space, exactly enumerating all objects is intractable.

Original: The joint distribution is then approximated by:(cid:88)
Revised: The joint distribution is then approximated by:

Original: [Equation]
Revised: [Equation]

Original: where o is found by a greedy search.
Revised: Note that we sum over all r ’s, and for each r we apply greedy search to pick the o that maximizes the likelihood.

Original: To get an explanation from the  model, we compute a posterior rational as follows:
Revised: To get an explanation from the [Table] model, we compute a posterior rational as follows:

Original: [Equation]
Revised: [Equation]

Original:  Experiments
Revised:  Experiments

Original: The goal of the system is to identify the intermediate reasoning step used in question answering.
Revised: The goal of the system is to identify the intermediate reasoning step used in question answering.

Original: We therefore experimentally evaluate two aspects of our model – accuracy of answers and correctness of reasoning steps.
Revised: We therefore experimentally evaluate two aspects of our model – accuracy of answers and correctness of reasoning steps.

Original: 4. Setup
Revised: 4. Setup

Original: Datasets.
Revised: Datasets.

Original: The relation model and the answer model are trained on the socialIQA dataset (Sap et al., 2019).
Revised: The relation model and the answer model are trained on the socialIQA dataset (Sap et al., 2019).

Original: SocialIQA has 37,588 multiplechoice questions that covers the pragmatic implications of everyday, social events.
Revised: SocialIQA has 37,588 multiplechoice questions that covers the pragmatic implications of everyday, social events.

Original: The object modelis trained on ATOMIC2020 (Hwang et al. , 2021),which consists of 1.33M RDF tuples about common entities and events with 23 unique relationtypes.
Revised: The object model is trained on ATOMIC2020 (Hwang et al., 2021), which consists of 1.33M RDF tuples about common entities and events with 23 unique relation types.

Original: However, the relation model only considersthe 16 relations related to social interactions.
Revised: However, the relation model only places a distribution over the 16 relations related to social interactions.

Original: Baselines.
Revised: Baselines.

Original: The baseline for comparing accuracyis a standard BERT base model with a multiplechoice head (Sap et al., 2019) (referred to as BERT).
Revised: The baseline for comparing accuracy is a standard BERT base model with a multiplechoice head (Sap et al., 2019) (referred to as BERT).

Original: Two baselines are considered for evaluating thereasoning steps.
Revised: Two baselines are considered for evaluating the reasoning steps.

Original: The first baseline associates eachquestion type  with a set of relations (referred toas rule-based).
Revised: Table 2 shows the first baseline where each question type associates with a set of relations (referred to as rule-based).

Original: The second baseline usesscoresprovided by the external KB for choosing the mostlikely commonsense inference (referred to asKBbased) (Bosselut et al., 2021).
Revised: The second baseline uses the object likelihoods provided by [Table] COMET (Bosselut et al., 2021) for choosing the most likely commonsense inference (referred to as

Original:  Implementation & Hyperparameters.
Revised: COMET). Implementation & Hyperparameters.

Original: We implement both latent variable and non-latent variable (baseline) models with BertModel in Huggingface Transformers (Wolf et al., 2020).
Revised: We implement both latent variable and non-latent variable (baseline) models with BertModel in Huggingface Transformers (Wolf et al., 2020).

Original: For the object model, we use the released BART-large modelon github.
Revised: For the object model, we use the released BART-large model on github.

Original: 2 We perform grid search with learning rates {5e-6, 1e-5, 3e-5, 5e-5} and batch sizes{1,2,3,4,8} to achieve best-possible performancefor the baseline model.
Revised: 2 We perform grid search with learning rates {5e-6, 1e-5, 3e-5, 5e-5} and batch sizes {1,2,3,4,8} to achieve best-possible performance for the baseline model.

Original: Due to limited computation resources, we only fine-tune the latent variablemodel with a learning rate of 1e-5 and batch sizesof {1,2,3}.
Revised: Due to limited computation resources, we only fine-tune the latent variable model with a learning rate of 1e-5 and batch sizes of {1,2,3}.

Original: For both models, we warm up the learning rate for first 10% steps and train for five epochs.
Revised: For both models, we warm up the learning rate for first 10% steps and train for five epochs.

Original: Evaluation metrics.
Revised: Evaluation metrics.

Original: To check the correctness ofthe reasoning steps, we annotate 500 test examplesin socialIQA: for each example, we label up tothree relations if they may lead to useful objectsthat help to reach the answer.
Revised: To check the correctness of the reasoning steps, we annotate 500 test examples in socialIQA: for each example, we label up to three relations if they may lead to useful objects that help to reach the answer.

Original: Furthermore, we alsoannotate if their subsequent objects are correct; thatis, the entire RDF triple is correct.
Revised: Furthermore, we also annotate if their subsequent objects are correct; that is, the entire RDF triple is correct.

Original: Each approachis allowed to choose three relations.
Revised: Each approach is allowed to choose three relations.

Original: If any of therelations is correct, then it is considered to identifythe correct knowledge type; if the objects followedfrom the relations are also correct, then it is considered to find the fully correct reasoning step.
Revised: If any of the relations is correct, then it is considered to identify the correct knowledge type; if the objects followed from the relations are also correct, then it is considered to find the fully correct reasoning step.

Original: 4. Results
Revised: 4. Results

Original: Table 2 summarizes the accuracy results.
Revised: Table 3 summarizes the accuracy results.

Original: For eachapproach, the test result obtained from evaluatingthe checkpoint with the best validation accuracyis reported.
Revised: For each approach, the test result obtained from evaluating the checkpoint with the best validation accuracy is reported.

Original: Our model achieves an accuracy of

Original: [Table] Reproduction and our generative model use the same framework. [Table] 63.13% whereas the baseline is 62.28%.
Revised: Our model achieves an accuracy of 63.13% whereas the baseline is 62.28%.

Original: Therefore, the latent-variable model is able to maintain similar accuracy to the pretrained model.
Revised: Therefore, the latent-variable model is able to maintain similar accuracy to the pretrained model.

Original: Table 3 shows the results on identifying reasoning steps.
Revised: Table 4 shows the results on identifying reasoning steps.

Original: For choosing a relation, our model improves 24.2% over the rule-based method, suggesting that our generative model has learned how to identify the relevant knowledge type for commonsense examples.
Revised: For choosing a relation, our model improves 24.2% over the rule-based method, suggest- [Table] Relation accuracy reflects if an approach has chosen the correct knowledge types, and RDF accuracy reflects if an approach has found the full reasoning steps.

Original: Furthermore, when comparing the 
Revised: ing that our generative model has learned how to identify the relevant knowledge type for commonsense examples.

Original:  RDF accuracy (i.e., the reasoning step is fully correct), our model improves 23.4% over KB-based scoring, serving as an evidence that our relation model and answer model have learned explanations specific to the task.
Revised: Furthermore, when comparing the RDF accuracy (i.e., the reasoning step is fully correct), our model improves 23.4% over COMET, serving as an evidence that our relation model and answer model have learned explanations specific to the task.

Original: In summary, our model is able to identify the correct reasoning step that is not explicitly present in the context but is required to derive the answer for a substantial number of cases without supervision.
Revised: In summary, our model is able to identify the correct reasoning step that is not explicitly present in the context but is required to derive the answer for a substantial number of cases without supervision.

Original:  Conclusion
Revised:  Conclusion

Original: We propose a latent-variable model to generate explanations for commonsense reasoning QA without supervision.
Revised: We propose a latent-variable model to generate explanations for commonsense reasoning QA without supervision.

Original: The experimental results show that our approach achieve similar accuracy to the pretrained model.
Revised: The experimental results show that our approach achieve similar accuracy to the pretrained model.

Original: The human evaluation suggests our model can identify the correct reasoning steps for significantly more examples than an existing unsupervised approach for producing explanations.
Revised: The human evaluation suggests our model can identify the correct reasoning steps for significantly more examples than an existing unsupervised approach for producing explanations.

Original:  A Appendix
Revised:  Acknowledgments

Original: Table 4 presents the rule-based baseline for identifying the reasoning steps.
Revised: We would like to thank Justin Chiu and Celine Lee for valuable feedback.