=Paper=
{{Paper
|id=Vol-3769/paper3
|storemode=property
|title=Argumentative Patterns in the Context of Dialogical Exchanges in the Financial Domain
|pdfUrl=https://ceur-ws.org/Vol-3769/paper3.pdf
|volume=Vol-3769
|authors=Giulia D'Agostino,Andrea Rocci
|dblpUrl=https://dblp.org/rec/conf/cmna/DAgostinoR24
}}
==Argumentative Patterns in the Context of Dialogical Exchanges in the Financial Domain==
https://ceur-ws.org/Vol-3769/paper3.pdf
Argumentative patterns in the context of dialogical
exchanges in the financial domain
Giulia D’Agostino1,∗ , Andrea Rocci1
1
Institute of Argumentation, Linguistics and Semiotics, Università della Svizzera italiana, Switzerland
Abstract
The study of argumentative practices in context requires a deep understanding of the characteristics
of the activity type under observation. To acquire such a knowledge, pragma-dialectics offers the mid-
level analytical tool of the argumentative pattern with particular emphasis on what appears to be a
prototypical argumentative pattern in a specific argumentative activity type. The current contribution
extends the traditional definition of a prototypical argumentative pattern and applies the notion to the
context of dialogical exchanges occurring over the activity type in the financial domain represented by
quarterly earnings calls. Upon characterization of the request of confirmation of inference (ROCOI) as a
highly recognisable basic prototypical pattern, the second part of the paper showcases an experiment of
automatic recognition of such a pattern in question units. Results are encouraging and support further
work in the direction of pattern mining.
Keywords
argumentation in context, argumentative patterns, prototypical patterns, financial communication,
earnings conference calls, question-and-answer, request of confirmation of inference
1. Introduction
The distinctive features of argumentation practices in specialist domains crucially include the
social ontology of the interactions concerned. This component is pivotal in domains, such as
finance, where the body of specialist knowledge being developed is ultimately oriented towards
practical decision-making, namely towards informing investment decisions, which result in
transactions (e.g., trades or other kinds of deals), which have institutional, legally enforceable
consequences (e.g., contracts). This leads to the centrality of construct such as the activity type
[1] for the modeling of domain specific argumentation, which has long being recognized in
argumentation studies [2].
While the notion of activity type can specify at a high level institutional commitments of the
arguers, genre conventions, as well as the incentives motivating the participants to argue, a
study of the features of specialist argumentation practices also requires more tactical mid-level
concepts to fathom the social ontology and the observable sequences of speech acts and chains
of inferences. In particular, such mid-level constructs are needed to capture and name recurrent
strategies used by arguers to navigate the constraints and affordances of the activity type in
pursuit of shared goals and individual incentives.
The 24th International Workshop on Computational Models of Natural Argument (CMNA‘24), September 17, 2024, Hagen,
Germany
∗
Corresponding author.
Envelope-Open giulia.dagostino@usi.ch (G. D’Agostino); andrea.rocci@usi.ch (A. Rocci)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
This paper adopts the notion of argumentative pattern as the pivotal notion to relate the
micro-analysis of arguments at the speech-act and inferential level with the constraints and
affordances of an economically impactful, highly institutionalized, argumentative activity type
in the financial domain, namely the earnings call. In the context of an empirical computational
study of argumentation in the domain of finance, argumentative patterns that appear to embody
contextually significant strategies represent a primary target of mining as well as a primary
input of analytics aimed at exploring these argumentative strategies on a large scale.
The first part of this article provides a general definition of the notion of argumentative
pattern and a characterization of its relation with activity types, together with an exemplification
of significant patterns of the earnings call. The second part present a case study of one such
pattern, including an experiment of automated recognition aimed at large scale mining.
2. Argumentative Patterns
The notion of argumentative pattern, first introduced by the pragma-dialectical theory [3],
provides a unit of analysis for describing a system of argumentative moves that responds to the
constraints and affordances of the activity type. According to pragma-dialectics [4]:
An argumentative pattern consists of a particular constellation of argumentative
moves in which, in dealing with a particular kind of difference of opinion, in defence
of a particular type of standpoint a particular argument scheme or combination of
argument schemes is used in a particular kind of argumentation structure.
Thus, in this account the identification and analysis of patterns mostly focuses on four
components: (a) the type of issue they address, (b) the semantic type of the standpoints being
argued for, (c) the structure of the argumentation supporting the standpoints and (d) the type
of argument schemes they mobilize. The literature also hints at a broader characterization
of argumentative patterns in terms of sequences of moves [5], and in this view patterns can
be considered as those routes or sequences of moves that participants (typically) choose in
a concrete activity type among those that are in principle argumentatively relevant in the
resolution of an argumentative discussion in a given context.
Pragma-dialectics furthermore specifies the nature of argumentative patterns in relation to
the activity type in terms of a twofold “typicity” [5, 6, 4], distinguishing between prototypical
patterns, which are directly or indirectly functional to realizing the institutional point of a certain
activity type and stereotypical patterns, which are not only prototypical but also significantly
frequent either in comparison with other activity types or with respect to other patterns in the
same activity type.
The current account builds on the broader idea of idea of patterns as consisting of sequences
of argumentatively relevant moves. Thus, in this account patterns may involve, but are not
necessarily limited to the combination of a standpoint type, an argument structure and the
associated argument scheme, as their characterization can involve any sequence of argumenta-
tively relevant moves, including those (e.g., challenges, critical questions, concessions) which
do not involve an argumentation structure or an argument scheme. Importantly, argumentative
patterns can emerge from dialectically significant sequences of moves across turns in dialogical
interaction, for instance, in the case study at issue, between question and answer turns. In
this respect, a distinction is made between “basic” patterns, which lie in a single turn, and
“dialogical” patterns that span across question-and-answer turns (the latter already defined
as such by [7]). In either case, prototypical argumentative patterns correspond to sequences
of argumentatively relevant moves that are compliant with the constraints and incentives of
the communicative exchange and fit (some of) the communicative goals of the activity type.
Additionally, the diagnostic criteria for prototypicality adopted here also take into account that
the pattern can be contextually recognized by the participants in the conversation themselves
as a conventional, recurrent strategy that specifically meets the purposes of the activity type; in
that case, the pattern is a also members’ category [8].
3. Activity type: Earnings Conference Calls
Earnings Conference Calls (ECCs) are teleconferences listed companies hold following the
publication of quarterly results, with the presence of financial analysts. Earnings calls have
been analysed [9] as an activity type in terms of their interaction field, i.e., the set of institutional
goals, commitments and rules associated with the event and the participant roles, and their
interaction scheme, i.e., the culturally shared script of the interaction, including turn-taking
rules and templates for individual turns. As for the interaction field, the ECC complements
the quarterly disclosures of the firm and, for managers, represents a way of discharging their
fiduciary duty towards shareholders, while analysts act on behalf of their investor clients.
Specific incentives to argue are associated to each role [7]: while managers have the incentive
to defend the market valuation of the firm and persuade shareholders that they deserve to
be entrusted with its tenure, analysts have potentially conflicting incentives to critically test
managers’ standpoints on behalf of their clients and to preserve an amicable relationship
with the managers. Regulations bind managers not to disclose hitherto unpublished material
information [10]; therefore, the entire conversation revolves around soft information such as
evaluative comments and, most importantly, arguments and explanations connecting the dots
between the already disclosed pieces of material information. The interaction scheme includes
an initial presentation by the managers, followed by a Q&A with the analysts [11], who take
turns at asking questions that are immediately answered by executives in the following turn(s).
The analysis of argumentative patterns occurring in the Q&A – object of the remainder of the
paper – first require that fine grained details of turn-taking and turn design are taken into
consideration.
3.1. Text Segmentation in Earnings Conference Calls
Typically, each analyst only has one turn for their questions. As a consequence, their turn
design adopts an idiosyncratic question-compression strategy whereby questions on multiple
topics are asked within a single turn before any response. Consequently, a question turn by
an analyst is a collection of sentences arranged around a number of topics, which triggers one
or more answering turns in response (e.g., different managers may choose to answer different
questions originating with the same question turn). This turn design and turn taking structure
poses a challenge to the analysis of argumentative discussions arising between managers and
analysts. In particular, mid-level segmentation of turns is required to produce argumentatively
relevant units from which patterns arise. Additionally, analysts themselves clearly mark their
turns as composed of thematic sub-units within the question turn.
These topically homogeneous sequences of utterances that compose the multi-issue question
turns can be called Maximal Interrogative Units (MIUs) [12, 13]: question units typically below
the level of the turn, but above the level of the clause or individual speech act. MIUs gather
a consecutive presentation of individual questions (sometimes mixed with non-interrogative
locutions) that may add colour or detail on a topic; constrain scope; and motivate, contextualise
or cross-reference. A Maximal Answering Unit (MAU) on the other hand, is a collection of
sentences within an answer turn that globally react to an MIU. An MAU can correspond to a
single sentence up to the entire turn.
As a direct consequence of this turn design, patterns in ECCs are unit-based and not turn-
based. In this context, the previously introduced term basic patterns designates patterns that fully
develop within a unit (either MIU or MAU), and thus are intra-unit patterns, whilst dialogical
patterns is used for patterns that develop between a MIU and a related MAU, and thus are
inter-unit patterns.
3.2. Argumentative Patterns in ECCs
Some examples of argumentative patterns in ECCs – both of the intra- and inter-unit type – are
presently proposed:
Prefaced questions Within an MIU, the argumentative structure is constituted by discursive
moves “preface” and “question”. A preface is an assertive statement that can either precede,
follow or be contained in a question, providing arguments supporting the relevance of the
speech act of the question [14, 15]. Example 1 (ABNB Q1 2021, analyst Justin Post) is an instance
of intra-unit argumentation in an MIU, which constitutes a basic argumentative pattern:
(1) ([I think in the letter, it said post listings were stable with Q4,]preface 1 but [it seems like
you’re really encouraged by what you’re seeing.]preface 2 )premises → ([So maybe you
could dive in there and tell us, you know, what is encouraging about what you’re seeing
with hosts]question 1 and [whether you see – expect a lot of new listings to hit the market
over the next year?]question 2 )conclusions
Here the two prefaces constitute the argumentative premises in support of the implicit
conclusion that the questions are relevant and deserving of an answer. The two questions are
thus the explicit counterpart of the conclusion of the inference.
Closed-list questions Closed-list questions – a category of questions variably called in the
literature alternative, multiple choice, or considered the sequence of multiple polar questions –
are initiators of a dialogical argumentative pattern. This means that the question in itself is a
recurrent structure, that is, a pattern, but it becomes an argumentative pattern only when paired
with the type of reply it receives [16].
In Example 2 (HAS Q1 2021), the reported MIU displays a closed-list question in boldface
type (tagged as question 2); the excerpt in the corresponding MAU accepts the second option
proposed by the question, that is, that the growth in the Magic Arena segment is a function of
the expansion of the market base. The dialogical pattern displayed by the example is therefore
the combination of three elements: (a) an alternative question ([17] provides further detail on
the categorization and analysis of closed list questions); (b) an answer compliant with both the
logical and pragmatic constraints posed by the question, i.e., that exactly one of the proposed
options is the correct answer. Particularly, this answer developed within the boundaries of
a well-formed reply confirms the second option to be correct, which (c) is the one carrying
positive implications.
(2) David Beckel (analyst)
I have two, if I could. First one, just on Arena or MAGIC in general, I guess. [Really
impressive growth, obviously, from Arena in the quarter.]preface 1 [I’m curious, do you
have the data sets of – capable of giving you a holistic picture of your player
base?]question 1 [I’m curious more specifically if that growth is coming at the expense
of tabletop or if you’re actually expanding the market base,]question 2 and [whether
or not you expect mobile to further expand the market base.]question 3 That’s my first
question.
Brian Goldner (CEO)
So in fact, you’re right. The Magic Arena had historically been expanding. It’s
accelerating in that effort. (...)
Allo-patterns Each argumentative pattern may have distinct practical realisations – that is,
they have allo-patterns. Due to their interactive nature, inter-unit patterns typically generate a
higher number of allo-patterns that may or may not be in finite number. For instance, the closed-
list question pattern just introduced – at least in the form initiated by a 2-option alternative
question – conceives four allo-patterns:
• ACCEPTANCE of the question framing; selection of the option with positive consequences
• ACCEPTANCE of the question framing; selection of the option with negative conse-
quences
• REJECTION of the question framing, refusing mutual opposition of the options; selection
of both options
• REJECTION of the question framing, claiming non-exhaustiveness of the options; selection
of neither option (possibly introducing and arguing for a third option)
On top of this, clearly, if one also took into consideration the allo-forms that a closed-list
question can embody, the resulting allo-patterns would be more numerous.
4. Case Study of a Prototypical Argumentative Pattern in ECCs
A Request of Confirmation of Inference (ROCOI, previously introduced and qualitatively studied
by [7]) is a intra-unit argumentative pattern in ECCs that is originated in MIUs. It is relevant
to the discussion in the sense that it appears to create an argumentative confrontation –
more specifically, a mixed confrontation [18]. The related inter-unit pattern derives from the
association of the ROCOI with the reply that it receives.
A ROCOI is an assertive question, i.e., in which a stance is asserted by the questioner, while
the interlocutor is asked to answer whether such a stance is correct. As a consequence, when it
is formulated directly, a ROCOI is invariably either a yes/no or closed-list question. Moreover,
ROCOIs represent a subcategory of assertive questions in the sense that they make explicit by
lexical means the fact that the stance asserted is the result of an inferential process – held either
by the questioner or a third party – and/or that what is requested to the interlocutor is not
simply a reaction on the validity or correctness of such an inference, but a clear (dis)confirmation
of the conclusion. This results in the ROCOI being a challenging question, regardless of the
degree of semantic indirectness with which is may be framed.
The ROCOI is a crucial argumentative pattern in ECCs because it structurally features argu-
mentation in the question formulation and, due to its challenging nature in a highly standardised
environment that prioritises unthreatening exchanges, it regularly elicits argumentative re-
sponses as well.
Based on its distinctive features, ROCOIs can be further distinguished into subcategories.
The current contribution will consider a two-way classification on the basis of the linguistic
indicators adopted in the question formulation.1 The two classes are:
1. Report of inferential operation (roughly correspondent to a merge of categories 1, 2 and
4 laid out by [7]). For a question to be included in this category, it must comply with
the following constraints: (a) the inferential reasoning is displayed in the question itself
(and not disconnected from it, being for instance part of a preface) and (b) the focus
of the request is on the conclusion or, at most, the inferential process that leads to the
conclusion; not on the premise(s) to the inference. An instance is shown in Example 3.
(3) Does that mean that customers are reluctant to term out these sort of prices?
2. Explicit request of (dis)confirmation (akin to what was already considered category 2 in
[7]). This comprises both end-of-sentence tags or phatic expressions such as “right?”, or
clauses such as “can you confirm (that)”, optionally followed by an assertive statement,
as displayed in Example 4.
(4) Can you confirm whether that is the case?
Moreover, a ROCOI is a prototypical argumentative pattern in the sense that: (a) such a
strategy is recognized as recurrent and typical by participants [19], (b) it pursues some of
the communicative goals associated to analysts in the activity type, such as challenging the
managers to produce argumentation (c) while respecting the analysts’ incentive to maintain
a good relationship with managers (since it typically is an indirect framing of a challenge),
even in the case in which the proposed inference is negative or plays the role of a bait for the
company’s management, i.e., it displays a not necessarily fully sincere “lifeboat” benevolent
interpretation of the situation which is focus of the question.
1
The driving choice behind such a distinction is the simplification of the categorization already provided in [7] and
ensure both robustness in the results and sufficiently large class size to allow the study; the tradeoff was realised by
generalising the linguistically-relevant surface characteristics of the original classes.
Presently displayed are the results from the study on the basic ROCOI – further work will
also delve into the dialogical relationship with the replies it receives.
4.1. Data and Method
Following, a brief presentation of an exploratory study on the automatic identification of MIUs
that contain at least an instance of ROCOI by fine-tuning on this task two pre-trained ML
models of the BERT family on three different input configurations (ablation study).
The dataset comprises 53 ECCs from years 2021-2023 for companies Airbnb (ABNB), British
Petroleum (BP), Credit Suisse (CS), Door Dash (DASH), Hasbro (HAS), Shell (SHEL), Exxon Mobil
(XOM) and Zillow (Z), for a total of 1210 MIUs. Manual annotation allowed for the identification
of 155 MIUs featuring ROCOIs; hereby 170 ROCOIs were gathered.2 The annotation was first
carried out by trained assistants, followed by an additional round of dictionary-based search of
instances – manually pruned of false positives – performed by the first contributor.3 Further
information about the annotation guidelines is provided in [21].
For each of the ROCOIs, the representation of both the entire MIU and the portion representing
the ROCOI itself was assigned a label, according to which of the two classes described above
they belonged to, for a total of 118 occurrences for class 1 and 52 occurrences for class 2. The
remaining MIUs that did not contain any ROCOI were assigned to a default class 0.
To partially overcome data imbalance both between the portions that do and do not contain
a ROCOI (labels 0 vs 1+2) and among the two ROCOI classes (labels 1 vs 2), two strategies were
implemented sequentially: first the application a K-Means algorithm to all the examples so to
group them in an unsupervised manner into clusters according to their embedding similarity;
this to ensure that clusters are evenly represented between train and test sets. Later, the majority
class of the train split was randomly undersampled at the level of the most numerous of the
minority classes, so to simulate balance among classes.4 Details on the training and testing sets
and the number of examples are provided in Table 1.
undersampled
total train test
train
class 0 1055 835 91 214
class 1 118 91 91 27
class 2 52 38 38 14
Table 1
Number of examples for training and testing.
The selected pre-trained models are roberta-large and deberta-v3-large. The models were first
2
The dataset is available on GitHub: https://github.com/dagosgi/ROCOIs/tree/main/CMNA2024.
3
Annotators are student assistants, employed with a part-time contract by the project that funds the current contri-
bution. They are second-year Master’s students in investor relations with a background in languages/linguistics.
Each document was analysed by two to four annotators in variable configuration. The resulting pairwise agreement
on the task of selecting the request type associated with a question is moderate to substantial, up to a Cohen’s
kappa [20] value equal to 0.76.
4
The minority classes were initially oversampled to the numerosity of the majority class but this approach led to
overfitting of the model to the least represented class and was thus discarded.
fine-tuned associating the label to the concatenation of the representation of the MIU and the
ROCOI (or the replication of the MIU itself for both fields, if the MIU did not contain a ROCOI).
The ablation study later consisted in (a) removing the MIU and training on the ROCOI+label
only and (b) removing the ROCOIs and training on the MIU+label only. The testing phase was
performed on the text of full MIUs in all cases.
Fine-tuning was performed over five epochs with batch size of 6, optimizing with AdamW
and setting the learning rate equal to 2e-5. The baseline against which the pre-trained models
are tested is SVM classification with TF-IDF vectorization setting the maximum number of
features at 300.
4.2. Results and Discussion
Table 2 displays the F1 values (harmonic mean of precision and recall) in the evaluation of
the prediction task for each of the classes on the test set. It shows that the RoBERTa model
outperforms the baseline, slightly privileging the majority class among the two ROCOI types –
this may also be influenced by the scarcity of examples for class 2 in both train and test sets. It is
surprising – but confirmed by multiple runs – that the DeBERTa model strongly underperforms
the baseline; such a result must be influenced by the inadequacy of the model for the task at
hand – perhaps due to the characteristics of its pre-training dataset.
class 0 class 1 class 2
model accuracy
(no ROCOI) (report ROCOI) (request ROCOI)
SVM 0.95 0.48 0.57 0.91
roberta-large 1.00 0.87 0.76 0.97
deberta-v3-large 0.89 0.05 0.00 0.80
Table 2
Micro F1 scores across models and classes and overall accuracy for the model.
Error analysis With reference to the results displayed in Table 2, two tables reporting the
confusion matrix for the RoBERTa and DeBERTa model outcomes respectively follow.
Table 3 shows that the classification performed by the RoBERTa model is robust: misclassifi-
cation only (marginally) affects the distinction between the two classes of ROCOI. This means
that binary identification of the presence vs. absence of ROCOIs is perfect.
class 0 class 1 class 2
(no ROCOI) (report ROCOI) (request ROCOI)
class 0 214 0 0
class 1 0 23 4
class 2 0 3 11
Table 3
Confusion matrix, RoBERTa model.
Table 4, on the other hand, confirms the unreliability of the DeBERTa classification. Whereas
the model identifies false positives for classes 1 and 2, it ultimately fails to recognize any type
of ROCOI, resorting to classify examples as the default class in nearly the totality of cases.
class 0 class 1 class 2
(no ROCOI) (report ROCOI) (request ROCOI)
class 0 204 9 1
class 1 26 1 0
class 2 14 0 0
Table 4
Confusion matrix, DeBERTa model.
Ablation study The results of the ablation study are reported in Table 5. The pattern that
emerges is that RoBERTa is agnostic towards which element of the pair is omitted in the training,
and in any case is not capable of satisfactory classification. On the other hand, DeBERTa shows
an unexpected better-than-chance performance in the classification of presence vs. absence
of the ROCOI in the case only the context, i.e., the MIU, is passed to the model in the training
phase. This is probably again linked to the characteristics of the model architecture and its
pre-training, and may be worth investigating in further research.
class 0 class 1 class 2
model accuracy
(no ROCOI) (report ROCOI) (request ROCOI)
Omitted feature:
ROCOI
roberta-large 0.00 0.19 0.00 0.11
deberta-v3-large 0.93 0.51 0.50 0.86
Omitted feature:
MIU
roberta-large 0.00 0.19 0.00 0.11
deberta-v3-large 0.89 0.06 0.00 0.81
Table 5
Micro F1 scores across models and classes and overall accuracy for the model in the ablation study.
Results appear to support the claim that the ROCOI is a clearly identifiable pattern and that,
with the support of the correct instrument for the task, it is possible to reliably classify an
interrogative unit as whether it features at least one ROCOI or not, even with a limited set of
examples. As expected, more robust results are obtained when feeding a model both the pattern
and the context (MIU) in which it is situated.
5. Conclusion and Future Work
The present contribution develops and updates the pragma-dialectical notion of argumenta-
tive pattern, implementing it within the specific turn taking and turn design constraints of a
specialized domain activity type.
The Q&A sessions of earnings conference calls (ECCs) are shown to feature some characteristic
argumentative patterns – both at the intra- and inter-unit level. The last part of the paper is
devoted to detailing an example of prototypical argumentative pattern of ECCs, namely the
request of confirmation of inference (ROCOI), and to an exploratory study that tests its automatic
recognition in question units. Results are in line with the expectation that an unmistakable
prototypical pattern is detectable with satisfactory results in the case of low-resource fine-tuning
of ML models of language.
Future work on this topic will include data augmentation as an alternative method to overcome
the physiological data imbalance across classes and the relative rarity of the pattern among
MIUs, as well as the extraction of the actual argumentative pattern from a question unit that
is acknowledged to contain (at least) one. Finally, the study will include answers to such an
intra-turn argumentative pattern and thus investigate its inter-turn counterpart as well.
Acknowledgments
The authors thank Chris Reed for practical advice on how to refine the final draft. Any errors
that remain are ours.
The work in this paper was supported by the Swiss National Science Foundation under the
project “Mining argumentative patterns in context. A large scale corpus study of Earnings
Conference Calls of listed companies” (grant n. 200857)
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