The AAAI 2023 Workshop on Representation Learning for Responsible Human-Centric AI (R2 HCAI)

Contextualized Multi-Step Commonsense Reasoning through Context Extension
Hecong Wang1

Erqian Xu2

Pinxin Liu1

Zijian Meng1

Zhen Bai1

12

Department of Computer Science, University of Rochester, Rochester, NY 14627 USA
Warner School of Education and Human Development, University of Rochester, NY 14627 USA
hwang99@ur.rochester.edu, {exu6, pliu23, zmeng7}@u.rochester.edu, zbai@cs.rochester.edu

Abstractbehind AI predictions. When AIs make a mistake, human
feedback to the erroneous reasoning allows AIs to learn andrecover from the mistake. These potentials of common sense
motivate our work into a commonsense reasoning engine capable of learning through human feedback.
In education and cognitive science, the development ofcommonsense reasoning abilities, especially those relating
to causality, is crucial for children and has been heavily
researched (Bonawitz et al. 2010; Kuhn 2012; Stoel, Boxtel, and Drie 2014; Shavlik et al. 2022). Despite advancements in AI commonsense reasoning, a typically developed young child often has more sophisticated commonsense reasoning ability than state-of-the-art AI systems. We
hope to leverage the established literature in education andchild development to explore the question: Can AIs, likechildren, learn human-like common sense by engaging in
explanation-seeking conversations with young children instory comprehension tasks? As an initial step to explore thisresearch question, we seek a simple, effective, and controllable (i.e., intuitive and safe for children) means for AIs togenerate chain-of-thought explanations to be offered during
the conversations.
Contemporary works on generating multi-step commonsense reasoning—to be used as external knowledge or explanations for classification and question-answering systems—
largely follow a search-based approach and heavily rely on
context-less commonsense knowledge resources (Paul andFrank 2019; Ji et al. 2020; Wang et al. 2020; Arabshahi et al.
2021). One major limitation of the resulting multi-step commonsense reasoning systems is that most of their inferences
are isolated (i.e., made without access to other inferences).
To illustrate, consider the first and the third reasoning steps1of Figure 1’s upper reasoning path. The third reasoning step
has no access to any part of the first reasoning step andis thus oblivious to its presence. Similar to how failing toincorporate long-term context results in repetitive and selfcontradictory texts in text generation (Holtzman et al. 2018),
isolated inferences are prone to repetitions and contradictions, lowering the resulting reasoning paths’ quality. Furthermore, those multi-step commonsense reasoning systems

Commonsense reasoning is essential for AIs to understand,predict, and respond to human behaviors in social situations.
Recent advancements in deep learning bring promising results in AI commonsense reasoning. Nevertheless, a young
child often has more sophisticated commonsense reasoning
ability than state-of-the-art AI systems. This observation motivates us to investigate if AIs can learn from interacting withchildren in developing human-level commonsense reasoning
capability. As a first step to launching this line of investigation, we develop a novel approach to generate contextualized
multi-step commonsense reasoning paths for social situations
conveyed via short stories. The resulting reasoning paths areintended to serve as an explainable media to enable laypersons to understand the underlying reasoning process behind
AI predictions and provide feedback to help align AIs withhuman ways of thinking in commonsense reasoning.

Introduction
Common sense is a quintessential human capacity and afundamental challenge of AI. There is a general consensus
among the research community that commonsense reasoning ability is necessary to achieve human-level performancein several key areas of AI (Davis and Marcus 2015). Advancements in deep learning, especially that of large language models, have caused a resurgence of interest in commonsense reasoning and yielded promising results (Rajani
et al. 2019; Bosselut et al. 2019; Shwartz et al. 2020). As
society rapidly adopts AI technologies, ensuring that AIs
are reliable and trustworthy has become increasingly important. Efforts on multi-step commonsense reasoning havebeen recognized to play an essential role in building such
AIs (Camburu et al. 2018; Majumder et al. 2022) due tocommon sense’s inherent interpretability and explainability.
Commonsense reasoning is vital to enable AIs to understand, predict, and respond to human behaviors in social
situations. These are essential to achieve natural and efficient interactions between humans and computers, which isof great interest to HCI. By acting as a common groundbetween humans and AIs where shared mental models canbe established, common sense makes commonsense reasoning an easily interpretable explainable medium and enableslaypersons to understand the underlying reasoning process1

Within this study, we will use inference and reasoning step interchangeably and assume that multi-step reasoning (path) includes
multiple inferences or reasoning steps.

1external information to improve downstream reasoning systems’ performance or explanations to make a system moreinterpretable (Paul and Frank 2019; Ji et al. 2020; Wanget al. 2020; Arabshahi et al. 2021). Some generate reasoning
paths using explicit traversals through commonsense knowledge graphs (Paul and Frank 2019; Ji et al. 2020); others
form reasoning paths by connecting commonsense knowledge triplets iteratively retrieved from triplet-generating language models (Wang et al. 2020; Arabshahi et al. 2021).
Studies often incorporate additional mechanisms and heuristics to impose desirable properties on the generated reasoning paths, which vary from study to study.
The exact algorithms and heuristics vary across studies,as they largely depend on the study’s intended commonsense reasoning tasks and chosen commonsense knowledge
resources. However, a common theme across multiple studies is formulating multi-step commonsense reasoning as asearch problem; we, therefore, adopt the same formulation
within the study. To help improve the generalizability of ourstudy, we aim to minimize the number of assumptions madeabout the reasoning algorithm and heuristics that could inherently limit the applicability of context extension and our
findings. As a result, we adopt random walks as our commonsense reasoner: by generating multi-step commonsense
reasoning through sampling from random walks, we rely
on no heuristics and can, in principle, generate all possible
reasoning paths. Although our study primarily focuses on
the generated reasoning path’s quality, the findings of Wanget al. (2020), which also employs random walks, suggestthat downstream tasks can leverage reasoning paths generated through randomness as external knowledge.

lack mechanisms to sustain contextualization over multiple
reasoning steps. The absence of such a mechanism could
lead to inconsistent inference performance, which can beobserved within Figure 1’s upper reasoning path, and impair search-based reasoners’ ability to adopt the emerging
contextualized commonsense knowledge resources.
To address the current research gap in contextualized
multi-step commonsense reasoning and as a first step towardbuilding a commonsense reasoning engine that can learn
from human feedback, we extend the work of GLUCOSE(Mostafazadeh et al. 2020) by proposing context extension.
Context extension extends GLUCOSE (Mostafazadeh et al.
2020) from generating short, single-step commonsense explanations for events of a story to generating long, multi-stepones. It incrementally augments the reasoning context withinferred information by integrating them back into the reasoning context in a logical and coherent manner. A smallscale human evaluation study shows that context extension
can lead to a statistically significant improvement in thehuman-perceived quality of the generated reasoning paths.

Related Work
Commonsense Reasoning
Commonsense knowledge graphs are a kind of commonsense knowledge resource that represents information
through triplets. They have seen widespread adoption andsuccessful application in the area of natural language processing (Speer, Chin, and Havasi 2018; Sap et al. 2019).
Knowledge triplets are a kind of knowledge representation
dominant in commonsense reasoning that encode commonsense knowledge as head-relation-tail triplets. For example, statements such as ”Apples are fruits.” and ”If someone buys a coffee, then they will drink it.” can be encoded
as (apple, is-a, fruit) and (PersonX buys acoffee, xEffect, drink the coffee), respectively. (Speer, Chin, and Havasi 2018; Sap et al. 2019).
Large language models trained on massive corpora ofnatural language texts have achieved state-of-the-art performances on numerous AI benchmarks (Raffel et al. 2020)and drastically affected the research community. Bosselut
et al. (2019) demonstrated that by fine-tuning language models to generate knowledge triplets found in commonsense
knowledge resources, they gain the ability to express theirimplicit commonsense knowledge in the same triplet format. These triplet-generating language models, often referred to as knowledge models, perform commonsense inference when provided with a head and a relation (Hwanget al. 2021). Compared to explicit commonsense knowledge
resources such as knowledge graphs, knowledge models cangenerate novel (and often valid) commonsense inferenceseven for previously unseen scenarios. Their generalizability made commonsense knowledge models a popular choice
as a commonsense knowledge resource (Hwang et al. 2021).
By chaining together multiple commonsense knowledge
triplets, one can form multi-step commonsense reasoning
paths that express additional information beyond individual knowledge triplets (Bauer, Wang, and Bansal 2018).
Multi-step commonsense reasoning paths can be used asChild Commonsense Reasoning about Causality
One form of commonsense reasoning the field of child development has extensively researched is causal reasoning.
Causal reasoning is the ability to construct cause-effect relations in the physical world and storytelling (Engel 1995;
Gordon, Bejan, and Sagae 2011; Reed et al. 2015). The
development of causal reasoning ensures children can perform effectively in academic learning (Stoel, Boxtel, andDrie 2014; Shavlik et al. 2022), as well as make sense of
the world (Kuhn 2012).
The literature also sheds light on potential mechanisms
through which AIs’ reasoning may be improved. First,
causal reasoning can be prompted through dialogue, and various methods have been proposed to facilitate the dialectic
process. Second, explanations require a higher level of reasoning capability than inferences and predictions and play
an essential role in how humans acquire causal reasoning.
Therefore, we plan to adopt the explanation-seeking conversation as the essential mechanism to facilitate the improvement of AI commonsense reasoning.
It is generally accepted that young children develop causal
reasoning through dialogue and conversation. Effective conversational approaches documented by prior empirical studies include: developing a set of causality-oriented pedagogical principles to engage the discussion in the classroom setting (Stoel, Boxtel, and Drie 2014); promoting the use of
dialogic scaffolds between students and educational prac2

No Context Extension

The man made pancakes. He
poured syrup on the pancakes. He
took a bite. He accidentally had
used raspberry syrup. He gave
the rest of the pancake to his
sister.

The man made pancakes. He
poured syrup on the pancakes. He
took a bite. He accidentally had
used raspberry syrup. He gave
the rest of the pancake to his
sister.

The man made pancakes. He
poured syrup on the pancakes. He
took a bite. He accidentally had
used raspberry syrup. He gave
the rest of the pancake to his
sister.

GLUCOSEGLUCOSEGLUCOSEThe man accidentally uses
raspberry syrup.

The man feel(s) regret.
CausesReasoning Step 1

The man feel(s) regret.
CausesReasoning Step 2

Results in
Reasoning Step 3

Contradiction
The man posses(es)pancakes.

Random Context Extension

Repetition

The man made pancakes. He
poured syrup on the pancakes. He
took a bite. He accidentally had
used raspberry syrup. He gave
the rest of the pancake to his
sister.

+

GLUCOSEThe man takes a bite.

The man made pancakes. He
poured syrup on the pancakes.
The man eats. He took a bite. He
accidentally had used raspberry
syrup. He gave the rest of thepancake to his sister.

GLUCOSEThe man eats.
Causes/Enables
Reasoning Step 1+

The man made pancakes. He
poured syrup on the pancakes.
The man eats. He took a bite. He
accidentally had used raspberry
syrup. The man has a bad taste.
He gave the rest of the pancake
to his sister.

GLUCOSEThe man has a bad taste.
Causes/Enables
Reasoning Step 2

Causes/Enables
Reasoning Step 3

The man gives thepancakes to his sister.

Figure 1: Example three-step commonsense reasoning paths generated by our system. The upper path was generated without
context extension, and all three reasoning steps use the same reasoning context; the lower path was generated using the random
implementation of context extension that randomly inserts the inferred information into the reasoning context. For the upper
path, only the first reasoning step is in-context reasoning (i.e., the premise is in the reasoning context), and the other two are
out-of-context reasoning (i.e., the premise is not in the reasoning context). Furthermore, the upper path exhibits issues such as
repetition and contradiction, and there is a noticeable quality difference between its first and the other two reasoning steps.
titioners (Pontecorvo and Girardet 1993; Kim 2016); harnessing visual representation such as diagram or mindmap
to elicit conversation that leads to relation constructions between information (Easterday, Aleven, and Scheines 2007;
Buzan 2018; van der Wilt, van der Nat, and van der Veen
2022); and leveraging hand puppet to trigger children’s
causal utterances in storytelling context (Reed et al. 2015).

forces, factors, and processes” (Wellman and Liu 2007) andis thus fundamental for children to generalize knowledge
and improve their understanding of causal structure. (Wellman and Liu 2007; Lombrozo and Vasilyeva 2017). When
explanation occurs, either to others or to themselves, humans are able to build causal knowledge and transfer extant
understanding to novel cases in a more productive manner(Williams and Lombrozo 2010; Walker et al. 2012; Legare,
Sobel, and Callanan 2017).

During the development of causal reasoning, it is critical for children to draw connections between an antecedentand its outcome (i.e., inference), predict the outcome of agiven antecedent (i.e., prediction), and explain the mechanism that leads to that outcome (i.e., explanation) (Gopnik et al. 2004; Bonawitz et al. 2010; Legare, Gelman, andWellman 2010). Among these three dimensions, the role ofexplanation—in causal forms like “why” questions or “because” statements—has recently gained special attention in
the field of child development (Hickling and Wellman 2001;
Wellman and Liu 2007; Legare, Sobel, and Callanan 2017).
Compared to inference and prediction, explanation requires
more abstract and higher-level reasoning capabilities since
it involves a “more general system or framework of causal

Contextualized Commonsense Reasoning
Prior works on contextualized commonsense reasoning areprimarily concerned with ensuring individual inferences areappropriate for the reasoning context (i.e., rational, relevant,and informative) (Ji et al. 2020). Several methods have been
proposed to ensure such local contextualization: using highquality commonsense knowledge resources for rational andinformative inferences; preferring or limiting inferences thatinvolve concepts that are semantically similar to those in thereasoning context; performing heuristic searches to find thereasoning paths with the highest total relevance (Paul and3

GLUCOSE organizes its commonsense knowledge alongten reasoning dimensions differentiated by their reasoning
aspects (i.e., cause and effect, naive psychology, location
state, possession state, and other aspects) and directions (i.e.,
forward and backward). Examples of specific statements
generated by GLUCOSE can be found in Figure 1; specially,
consider the second reasoning step of the lower reasoning path: The man eats >Causes/Enables> The
man has a bad taste is a reasoning that is specificto the story context. Mostafazadeh et al. (2020) showedthat GLUCOSE could be used to fine-tune language models
to generate commonsense explanations that rival humans’
when provided with a story context, a premise sentence, anda reasoning dimension.
In addition to sharing little overlap with existing commonsense knowledge resources (Mostafazadeh et al. 2020),
namely ConceptNet (Speer, Chin, and Havasi 2018) and
ATOMIC (Sap et al. 2019), GLUCOSE distinguishes itself from these resources in two ways: it uses a semistructured knowledge representation and captures contextualized commonsense knowledge. Unlike conventional
knowledge triplets, whose heads and tails are words orphrases, GLUCOSE uses natural language sentences withpredefined structures as its triplets’ heads and tails, enabling them to capture richer commonsense knowledge—
GLUCOSE adopts a more expressive knowledge representation than other commonsense knowledge resources. As all
GLUCOSE commonsense knowledge is grounded in stories
from the ROCStories Corpus (Mostafazadeh et al. 2016),
GLUCOSE implicitly captures the subtle notions of contextualized commonsense knowledge—all GLUCOSE commonsense knowledge is relevant, informative, and logicallyconsistent with respect to its narrative context.
We adopt a GLUCOSE-fine-tuned T5 language model as
the contextualized knowledge model used throughout ourevaluations. We only make use of the specific statement partsof the generated explanations, which are sufficient for the
purposes of our study. Additionally, we only make use of thefive forward reasoning dimensions of GLUCOSE (i.e., dimensions 6 through 10), as production systems are forwardchaining reasoning systems. However, it is important to notethat context extension is not limited to forward reasoning.

Frank 2019; Ji et al. 2020; Wang et al. 2020). In this study,
we also pay attention to global contextualization: ensuring
all the individual inferences are appropriate for each otherand form coherent reasoning when used together (e.g., norepeated or contradictory inferences).

Context Extension
With the ultimate goal of building a conversational agent toparticipate in explanation-seeking conversations with young
children in story comprehension tasks, we present context
extension: a mechanism, inspired by production systems,
that introduces context into search-based multi-step commonsense reasoning approaches. The mechanism’s operation is simple and intuitive; the operation is also controllable
as it is primarily symbolic.

Motivation
Existing multi-step commonsense reasoning systems makemost of their inferences in isolation from each other—a reasoning step is not made in consideration of all other reasoning steps; this leads to issues such as repetitions and contradictions, as Figure 1 illustrates. Furthermore, as existing
multi-step commonsense reasoners mainly rely on contextless commonsense knowledge resources, they lack mechanisms to sustain contextualization over multiple reasoning
steps, which could impair their ability to adopt contextualized knowledge resources. We hope to address these two issues by taking inspiration from production systems.
Production systems are forward-chaining reasoning systems popular in cognitive architectures (Laird 2012). Such asystem models human short-term and long-term memory using its working memory and knowledge base, respectively. It
operates by iteratively modifying the working memory using
productions (i.e., if-then instructions) from the knowledge
base until deriving the desired statement (Russell, Norvig,
and Davis 2010). Production systems often require a control component to help resolve competing productions. We
draw parallels between the components of production systems and that of contextualized multi-step commonsense
reasoners: the working memory, the knowledge base, and the
productions correspond to the reasoning context, the knowledge model, and the knowledge triplets. However, unlike theworking memory (e.g., sets of logical statements), the reasoning context (e.g., natural language sentences) is usuallyorder-sensitive. This difference is the primary motivation behind context extension: how to appropriately extend ordersensitive reasoning contexts, such as stories?

Random Walks Reasoner
Reiterating an earlier point, to help improve the generalizability of our study, we minimize the number of assumptions
made about the reasoning algorithm and heuristics, whichvary across studies, that could inherently limit the applicability of context extension and our findings. We, therefore,
adopt random walks as our commonsense reasoner, imposing minimal assumptions on the algorithms and heuristics.
From an implementation perspective, the GLUCOSEfine-tuned T5 language model, which serves as our contextualized knowledge model, can be viewed as a tripletgenerating function KM with three parameters:

GLUCOSE Contextualized Knowledge Model
The GeneraLized and COntextualized Story Explanations(GLUCOSE) (Mostafazadeh et al. 2020) is a large collectionof more than 670K story-specific commonsense knowledge
curated through crowdsourcing. It captures implicit commonsense knowledge using semi-structured inference rules,
each consisting of a specific statement (i.e., story grounded
explanation) and a corresponding general rule (i.e., generalized commonsense knowledge). Both specific statements
and general rules adopt a knowledge triplet representation.

• Context the narrative context (i.e., a list of sentences),• Premise the premise sentence (i.e., a sentence), and• Dimension the reasoning dimension (i.e., an integer).
4

Algorithm 1: Random walk reasoner (no context extension).

• Causal Random This implementation randomly inserts
the new sentence into the current reasoning context
somewhere before or somewhere after the sentence thatitself that itself is the cause of or effect of, respectively.
This implementation ensures the coherence of the generated context from a causal perspective.
• Causal Adjacent This implementation inserts the new
sentence into the current reasoning context immediately
before or after the sentence that itself is the cause of or
effect of, respectively. This implementation ensures thecoherence of the generated context from both a causal
and a centering (Grosz, Joshi, and Weinstein 1995) perspectives.

function R EASONER(Context, Length)
P ath ← []P remise ← R ANDOM S ELECT (Context)
for i ← 1, Length do
Dim ← R ANDOM S ELECT ([6, 7, 8, 9, 10])
T riplet ← KM (Context, P remise, Dim)
P ath.APPEND (T riplet)
P remise ← T riplet[2]end forreturn P ath
end function

From an implementation perspective, all three implementations of context extension can be viewed as a function CE
with three parameters:

As our study only leverages the forward reasoning dimensions, the resulting triplets of KM will always be of the form
(premise, relation, conclusion) where premise is the cause of
conclusion and relation is a dimension-specific label.
Algorithm 1 shows a random-walk-based multi-step commonsense reasoner with two parameters:• Context a list of strings representing the reasoning context. For GLUCOSE, a list of sentences represents thenarrative context; and• Length an integer specifying the length of the resulting
reasoning path, which is represented as a list of triplets.
The function generates a list of triplets iteratively retrieved
from the knowledge model KM, which can then be connected to form a reasoning path.

• Context the current context (i.e., a list of sentences),• Conclusion the conclusion sentence (i.e., a sentence), and• Information additional information about the causal relationship between the sentences (e.g., A preceeds B, denoted as A ≺ B; and A succeeds B, denoted as A ≻ B).
We show in Algorithm 2 how context extension CE can be
added to our random-walk-based reasoner.

Evaluation
We used the same knowledge model (i.e., T5-large using
greedy decoding (Raffel et al. 2020)) throughout our study
as a control variable and used context extension as an independent variable.
In search-based multi-step commonsense reasoning, theeffects of incorrect inferences will be amplified by each reasoning step, as later inferences are conditioned on earlier
ones. It is, therefore, important to ensure the performanceof the knowledge model used. We defer the detailed discussion of our fine-tuning process to the appendix. However,to summarize, we discovered that oversampling is an effective method to combat the imbalance of training data acrossdifferent reasoning dimensions in GLUCOSE.
We hypothesize that context extension can improve thehuman-perceived quality of the generated multi-step commonsense reasoning paths. To verify this hypothesis, we
randomly sampled and then manually evaluated three-step
commonsense reasoning paths using each proposed context extension implementation. We randomly selected 50
stories from the ROCStories Corpus (Mostafazadeh et al.
2016), which GLUCOSE was built upon (Mostafazadeh
et al. 2020), to be used as the reasoning contexts. For eachselected story, we performed the following evaluation procedure:Context Extension
Algorithm 2: Random walk reasoner with context extension.
function R EASONER(Context, Length)
P ath ← []P remise ← R ANDOM S ELECT (Context)
for i ← 1, Length do
Dim ← R ANDOM S ELECT ([6, 7, 8, 9, 10])
T riplet ← KM (Context, P remise, Dim)
P ath.APPEND (T riplet)
P remise ← T riplet[2]Context ← CE(
Context, T riplet[2], T riplet[1] ≺ T riplet[2])end forreturn P ath
end function
After each reasoning step, context extension augments
the reasoning context with the inferred information (i.e., theconclusion) by inserting it into the reasoning context following specific rules. We provide the causal relationship between the premise and conclusion as additional contextual
information to the context extension mechanism. We propose three implementations of context extension with an increasing level of logicality and coherence.
• Random This implementation randomly inserts the new
sentence into the current reasoning context. We view thisas a baseline implementation.

1. We generated three-step commonsense reasoning paths
through our reasoner conditioned on the context extension implementation used (i.e., no extension, random,
causal random, and causal adjacent).
2. We randomly shuffled the four reasoning paths and presented them to an expert evaluator along with the reasoning context (i.e., the story).
5

Score count

Score distribution (%)

Statistics

Experiment

Condition

0

1

2

3

Total

0

1

2

3

AVG

STD

Context extension

No extension
RandomCausal random
Causal adjacent

66
68
52
40

70
85
72
93

87
64
78
81

27
33
48
36

250
250
250
250

26.4
27.2
20.8
16.0

28.0
34.0
28.8
37.2

34.8
25.6
31.2
32.4

10.8
13.2
19.2
14.4

1.300
1.248
1.488
1.452

0.979
1.000
1.027
0.927

Table 1: Summary of the human evaluation results. The highest average quality rating is highlighted.

Limitation and Future Work

3. The expert evaluator indicates their perceived quality foreach path using a four-point Likert scale, where zero indicates that the reasoning path is completely nonsensicaland three indicates that the reasoning path is human-like.
A similar Likert scale was used during the human evaluation of Mostafazadeh et al. (2020).

Although they improve the quality of the resulting reasoning paths, the proposed context extensions are still preliminary and leave huge room for improvement. For example,
more sophisticated methods, such as temporal graph extraction, could be used to analyze and propose sentence insertion
locations. Currently, context extension is mainly intended
for GLUCOSE (Mostafazadeh et al. 2020) or similar contextualized commonsense knowledge models. However, it
is possible to integrate commonsense knowledge retrievedor generated from other knowledge resources, such as ConceptNet (Speer, Chin, and Havasi 2018) and ATOMIC (Sapet al. 2019; Hwang et al. 2021), during the extension process.
The evaluation of the technique should also be extended toinclude backward reasoning dimensions and involve moreevaluators.
The generated reasoning paths and their associatedhuman-evaluated quality score already permit a preliminary investigation into children’s reactions toward machinegenerated commonsense reasoning paths. We intend to carryout this investigation which will inform us of the appropriate
interaction and elicitation strategies for gathering concrete
feedback from children.

We repeated the procedure five times per story for all 50
stories, which resulted in 1000 manually evaluated threestep reasoning paths evenly distributed across stories and thecontext extension implementation used.

ResultThe Friedman test suggests that there exists a statisticallysignificant difference among the four context extension implementations (p = 0.012). As a result, we conducted posthoc pairwise comparisons using Wilcoxon signed-rank test,
which revealed that
• Random context extension negatively impacted the reasoning quality compared to no context extension;• Both causal random and causal adjacent context extension yielded better reasoning compared to other methods;• There is no statistically significant difference between
causal random and causal adjacent context extension.
However, causal random scored slightly higher thancausal adjacent, but causal adjacent is more consistentthan causal random.

Conclusion
Multi-step commonsense reasoning may play an essential
role in building reliable and explainable AIs for the future. To bridge the current research gap between locally andglobally contextualization multi-step commonsense reasoning, we propose context-extension, a mechanism to enablesearch-based multi-step commonsense reasoners to leverage
the emerging contextualized commonsense knowledge resources. Results from a small-scale human evaluation showthat our mechanism improves reasoning quality by a statistically significant margin. However, substantial refinement to
the mechanism can still be made, leading to exciting future
research opportunities.

Discussion
Context extension incrementally augments the reasoning
context with inferred information by integrating them backinto the reasoning context in a logical and coherent manner.
We attribute the performance gain from context extension to
the following two factors:• When coupled with a contextualized knowledge model,
context extension allows inferences to be made in consideration of all prior inferences, thus enforcing global
contextualization (i.e., ensuring all the individual inferences are appropriate for each other and form coherent
reasoning when used together).
• By integrating prior inferences into the reasoning context, the premises for future inferences (which are basedon prior inferences) are guaranteed to be within the reasoning context. This invariant better aligns with the assumptions of contextualized knowledge model, thus indirectly ensuring inference quality.

References
Arabshahi, F.; Lee, J.; Bosselut, A.; Choi, Y.; and Mitchell,
T. 2021. Conversational Multi-Hop Reasoning with Neural
Commonsense Knowledge and Symbolic Logic Rules. In
Proceedings of the 2021 Conference on Empirical Methods
in Natural Language Processing, 7404–7418. Online andPunta Cana, Dominican Republic: Association for Computational Linguistics.
6

Hwang, J. D.; Bhagavatula, C.; Bras, R. L.; Da, J.; Sakaguchi, K.; Bosselut, A.; and Choi, Y. 2021. COMETATOMIC 2020: On Symbolic and Neural Commonsense
Knowledge Graphs. In AAAI.
Ji, H.; Ke, P.; Huang, S.; Wei, F.; and Huang, M. 2020. Generating Commonsense Explanation by Extracting Bridge
Concepts from Reasoning Paths. In AACL.
Kim, M. 2016. Children’s Reasoning as Collective Social Action through Problem Solving in Grade 2/3 Science
Classrooms. International Journal of Science Education,
38(1): 51–72.
Kuhn, D. 2012. The development of causal reasoning.
WIREs Cognitive Science, 3(3): 327–335.
Laird, J. E. 2012. The Soar Cognitive Architecture.
Legare, C. H.; Gelman, S. A.; and Wellman, H. M. 2010. Inconsistency with prior knowledge triggers children’s causal
explanatory reasoning. Child Development, 81(3): 929–944.
Legare, C. H.; Sobel, D. M.; and Callanan, M. 2017. Causal
learning is collaborative: Examining explanation and exploration in social contexts. Psychonomic bulletin review,24(5): 1548–1554.
Lombrozo, T.; and Vasilyeva, N. 2017. Causal Explanation.
In Waldmann, M., ed., The Oxford Handbook of Causal Reasoning, Oxford handbooks online, 415–432. Oxford University Press. ISBN 9780199399550.
Majumder, B. P.; Camburu, O.; Lukasiewicz, T.;
and Mcauley, J. 2022.
Knowledge-Grounded SelfRationalization via Extractive and Natural Language
Explanations. In Proceedings of the 39th International
Conference on Machine Learning, 14786–14801. PMLR.
ISSN: 2640-3498.
Mostafazadeh, N.; Chambers, N.; He, X.; Parikh, D.; Batra,
D.; Vanderwende, L.; Kohli, P.; and Allen, J. F. 2016. A
Corpus and Cloze Evaluation for Deeper Understanding ofCommonsense Stories. In NAACL.
Mostafazadeh, N.; Kalyanpur, A.; Moon, L.; Buchanan, D.;
Berkowitz, L.; Biran, O.; and Chu-Carroll, J. 2020. GLUCOSE: GeneraLized and COntextualized Story Explanations. In Proceedings of the 2020 Conference on Empirical
Methods in Natural Language Processing (EMNLP), 4569–
4586. Online: Association for Computational Linguistics.
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002.
BLEU: A Method for Automatic Evaluation of MachineTranslation. In Proceedings of the 40th Annual Meetingon Association for Computational Linguistics, ACL ’02,
311–318. USA: Association for Computational Linguistics.
Paul, D.; and Frank, A. 2019. Ranking and Selecting MultiHop Knowledge Paths to Better Predict Human Needs. In
Proceedings of the 2019 Conference of the North American
Chapter of the Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long and Short
Papers), 3671–3681. Minneapolis, Minnesota: Association
for Computational Linguistics.
Pontecorvo, C.; and Girardet, H. 1993. Arguing and Reasoning in Understanding Historical Topics. Cognition andInstruction, 11(3-4): 365–395.

Bauer, L.; Wang, Y.; and Bansal, M. 2018. Commonsense
for Generative Multi-Hop Question Answering Tasks. In
EMNLP.
Bonawitz, E.; Ferranti, D.; Saxe, R.; Gopnik, A.; Meltzoff,
A.; Woodward, J.; and Schulz, L. 2010. Just Do It? Investigating the Gap Between Prediction and Action in Toddlers’Causal Inferences. Cognition, 115: 104–17.
Bosselut, A.; Rashkin, H.; Sap, M.; Malaviya, C.; Celikyilmaz, A.; and Choi, Y. 2019. COMET: Commonsense Transformers for Automatic Knowledge Graph Construction. In
Proceedings of the 57th Annual Meeting of the Association
for Computational Linguistics, 4762–4779. Florence, Italy:
Association for Computational Linguistics.
Buzan, T. 2018. Mind Map Mastery: The Complete Guide
to Learning and Using the Most Powerful Thinking Tool in
the Universe. Watkins Media. ISBN 9781786781529.
Camburu, O.-M.; Rocktäschel, T.; Lukasiewicz, T.; andBlunsom, P. 2018. e-SNLI: Natural Language Inferencewith Natural Language Explanations. In Advances in Neural
Information Processing Systems, volume 31. Curran Associates, Inc.
Davis, E.; and Marcus, G. 2015. Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence.
Communications of the ACM, 58(9): 92–103.
Easterday, M. W.; Aleven, V.; and Scheines, R. 2007. ’Tis
Better to Construct than to Receive? The Effects of Diagram
Tools on Causal Reasoning. In Proceedings of the 2007
Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, 93–100.
NLD: IOS Press. ISBN 9781586037642.
Engel, S. 1995. The Stories Children Tell: Making Sense Of
The Narratives Of Childhood. Henry Holt and Company.
ISBN 9781466813137.
Gopnik, A.; Glymour, C.; Sobel, D.; Schulz, L.; Kushnir, T.;
and Danks, D. 2004. A Theory of Causal Learning in Children: Causal Maps and Bayes Nets. Psychological review,
111: 3–32.
Gordon, A.; Bejan, C.; and Sagae, K. 2011. Commonsense
Causal Reasoning Using Millions of Personal Stories. Proceedings of the AAAI Conference on Artificial Intelligence,25(1): 1180–1185.
Grosz, B. J.; Joshi, A. K.; and Weinstein, S. 1995. Centering:
A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics, 21(2): 203–225. Place:Cambridge, MA Publisher: MIT Press.
Hickling, A. K.; and Wellman, H. M. 2001. The emergence of children’s causal explanations and theories: evidence from everyday conversation. Developmental Psychology, 37(5): 668–683.
Holtzman, A.; Buys, J.; Forbes, M.; Bosselut, A.; Golub, D.;
and Choi, Y. 2018. Learning to Write with Cooperative Discriminators. In Proceedings of the 56th Annual Meeting of
the Association for Computational Linguistics (Volume 1:
Long Papers), 1638–1649. Melbourne, Australia: Association for Computational Linguistics.
7

Wellman, H.; and Liu, D. 2007. Causal Reasoning as Informed by the Early Development of Explanations, 261–279.
ISBN 9780195176803.
Williams, J. J.; and Lombrozo, T. 2010. The role of explanation in discovery and generalization: Evidence from category learning. Cognitive Science, 34: 776–806. Place:
United Kingdom Publisher: Wiley-Blackwell PublishingLtd.

Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.;
Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020. Exploring the Limits of Transfer Learning with a Unified Textto-Text Transformer. arXiv:1910.10683 [cs, stat]. ArXiv:
1910.10683.
Rajani, N. F.; McCann, B.; Xiong, C.; and Socher, R. 2019.
Explain Yourself! Leveraging Language Models for Commonsense Reasoning. In Proceedings of the 57th Annual
Meeting of the Association for Computational Linguistics,
4932–4942. Florence, Italy: Association for Computational
Linguistics.
Reed, H.; Hurks, P.; Kirschner, P.; and Jolles, J. 2015.
Preschoolers’ Causal Reasoning During Shared PictureBook Storytelling: A Cross-Case Comparison Descriptive
Study. Journal of Research in Childhood Education, 29.
Russell, S. J.; Norvig, P.; and Davis, E. 2010. Artificial intelligence: a modern approach. Prentice Hall series in artificial
intelligence. Upper Saddle River: Prentice Hall, 3rd edition.
ISBN 978-0-13-604259-4.
Sap, M.; Le Bras, R.; Allaway, E.; Bhagavatula, C.; Lourie,N.; Rashkin, H.; Roof, B.; Smith, N. A.; and Choi, Y. 2019.
ATOMIC: An Atlas of Machine Commonsense for If-Then
Reasoning. Proceedings of the AAAI Conference on Artificial Intelligence, 33: 3027–3035.
Shavlik, M.; Özgün Köksal; French, B. F.; Haden, C. A.;
Legare, C. H.; and Booth, A. E. 2022. Contributions of
causal reasoning to early scientific literacy. Journal of Experimental Child Psychology, 224: 105509.
Shwartz, V.; West, P.; Le Bras, R.; Bhagavatula, C.; andChoi, Y. 2020. Unsupervised Commonsense Question Answering with Self-Talk. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4615–4629. Online: Association for Computational Linguistics.
Speer, R.; Chin, J.; and Havasi, C. 2018. ConceptNet
5.5: An Open Multilingual Graph of General Knowledge.
arXiv:1612.03975 [cs]. ArXiv: 1612.03975.
Stoel, G.; Boxtel, C.; and Drie, J. 2014. Teaching towards
historical expertise. Developing a pedagogy for fostering
causal reasoning in history. Curriculum Studies, 47: 49–76.
van der Wilt, F.; van der Nat, M. S.; and van der Veen, C.
2022. Shared Book Reading in Early Childhood Education:
Effect of Two Approaches on Children’s Language Competence, Story Comprehension, and Causal Reasoning. Journal of Research in Childhood Education, 36: 592–610.
Walker, C. M.; Williams, J. J.; Lombrozo, T.; and Gopnik,
A. 2012. Explaining influences children’s reliance on evidence and prior knowledge in causal induction. In Proceedings of the Annual Meeting of the Cognitive Science Society,volume 34.
Wang, P.; Peng, N.; Ilievski, F.; Szekely, P.; and Ren, X.
2020. Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering. In Findingsof the Association for Computational Linguistics: EMNLP
2020, 4129–4140. Online: Association for Computational
Linguistics.

Knowledge Model Fine-Tuning
We designed our language model prompt based on the original T5’s prompt design for SQuAD (Raffel et al. 2020)
with additional fields added to provide GLUCOSE-specificinformation: “glucose dimension: {dimension} question:{dimension-specific question} *{sentence}* context: {story
context}.” Table 2 lists the dimension-specific question prefixes we used during the study.
We curated our training and validation dataset as follows:
1. To ensure the quality of the inferences, we only used thesubset of GLUCOSE where the quality score is at least2, which we refer to as the quality subset.
2. Using stratified sampling, ensuring a similar distributionof reasoning dimensions and premise sentence position
between the two datasets, 10% of the quality subset was
sampled to be used as the validation set. We also ensured
that no stories were shared between the two datasets toprevent data leakage.
3. Finally, to combat the imbalance of data between different GLUCOSE reasoning dimensions, we augmented thetraining set via random oversampling on all minority reasoning dimensions.
We fine-tuned the model using the same hyperparameters and optimizer settings from Raffel et al. (2020), which
was also used by Mostafazadeh et al. (2020). We evaluatedthe perplexity of the model on the validation set at the endof each epoch and performed early stopping when the perplexity stopped decreasing for three consecutive epochs. We
adopt the same automatic evaluation used by Mostafazadeh
Dim
1
2
3
4
5
6
7
8
9
10

Question prefix
What is the event that directly causes or enables
What is the emotion or basic human drive that motivates

What is the location state that enables
What is the possession state that enables
What is the attribute that enables
What is the event directly caused or enabled by
What is the emotion that is caused by
What is the location state that results from
What is the possession state that results from
What is the attribute that results from

Table 2: Dimension-specific question prefixes used throughout our study. We derived these prefixed from the meaning of each GLUCOSE reasoning dimension shown in
Mostafazadeh et al. (2020).
8

Specific statement

General rule

Dim

Original

Ours

Original

Ours

1
2
3
4
5
6
7
8
9
10

72.5
73.9
73.8
79.3
70.5
80.2
81.1
86.6
71.7
66.9

74.6
75.6
76.5
84.0
73.5
77.7
82.5
85.3
89.6
70.7

66.4
67.6
68.5
73.0
69.8
77.6
76.8
86.8
68.6
57.5

67.9
72.0
73.3
77.9
72.0
70.3
74.5
81.0
88.0
73.6

Table 3: Evaluation of our model following the same setup
as Mostafazadeh et al. (2020). Higher scores are highlighted.
et al. (2020) (i.e., BLEU (Papineni et al. 2002)). We compared our model’s performance with the best model reported
in Mostafazadeh et al. (2020) in Table 3. On average, ourmodel outperforms the original by 4.68% for specific inference and by 6.30% for general inference.

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