Figure 1 - uploaded by Pavithra Rajendran
Content may be subject to copyright.
The relationship between explicit/implicit opinions and arguments/enthymemes.
Source publication
Enthymemes, that are arguments with missing premises, are common in natural language text. They pose a challenge for the field of argument mining, which aims to extract arguments from such text. If we can detect whether a premise is missing in an argument, then we can either fill the missing premise from similar/related arguments, or discard such e...
Contexts in source publication
Context 1
... proposal for enthymeme detection via opin- ion classification is illustrated in Figure 1, and consists of the following two steps. This assumes a separate process to extract the ("predefined") con- clusion, for example from the rating that the hotel is given. ...
Similar publications
In this paper, the perception of the /ae/-/ɛ/ vowel continuum was analysed in British and United States English speakers by testing their word identification across the pan-pen continuum. A clear difference was found between the two speaker groups, with the U.S. speakers continuing to perceive 'pan' beyond the British speakers, presumably due to /a...
In this paper, we investigate the multi-variate sequence classification problem from a multi-instance learning perspective. Real-world sequential data commonly show discriminative patterns only at specific time periods. For instance, we can identify a cropland during its growing season, but it looks similar to a barren land after harvest or before...
Action recognition has received increasing attention during the last decade. Various approaches have been proposed to encode the videos that contain actions, among which self-similarity matrices (SSMs) have shown very good performance by encoding the dynamics of the video. However, SSMs become sensitive when there is a very large view change. In th...
In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby po...
Citations
... There is also practical merit in using IAT CI : It provides for instance access to Argument Web infrastructure (Reed et al. 2017), makes CIs available for argumentation computation, e.g., as in TOAST , ArgSemSat (Giacomin et al. 2014), Tweety (Thimm 2017) and Argument Analytics (Lawrence et al. 2016). Having a solid and well-motivated representation of the interaction of CIs and argumentation also allows us to incorporate CIs for training mining algorithms (Budzynska et al. 2014;Gemechu and Reed 2019;Lippi and Torroni 2015), extending related work in the field of argument mining, where only a small number of approaches have dealt with the identification and reconstruction of implicit premises: Razuvayevskaya and Teufel (2016) manually reconstruct them in explicitly marked arguments, Feng and Hirst (2011) use argumentation schemes to identify them, Rajendran et al. (2016) differentiate between explicit and implicit opinions in order to surface them, Green (2017) reconstructs premises and conclusions in genetics research articles and Becker et al. (2020) use background knowledge for enthymeme reconstruction. Hautli-Janisz and El-Assady (2017) show that CIs can be identified automatically-the challenge remains in the exact spell-out of the implicated proposition. ...
Despite the ubiquity of conventional implicatures in language and the critical role they play in argumentation, they have heretofore been almost entirely absent from theories of argument and the linguistic expression of reasoning. In this paper, we discuss conventional implicatures (cis) as an interesting phenomenon at the interface of semantics, pragmatics and argumentation by harnessing research in semantics and pragmatics and extending an existing account for argument diagramming with this type of implicit meaning. In particular, we show that cis are unlike enthymemes, which are extremely challenging to conceptualise and to specify precisely. Instead, cis are anchored on the linguistic surface, trigger a largely predictable discourse contribution and are therefore more apt for argument analysis. By surfacing conventionally implicated material, we can unpack a wider variety of ways in which arguments are triggered by, composed of, and demolished by implicit discourse material, in particular inferential structures, conflicts and references to ethos. This also allows us to model the complex interplay between conventional implicature and argumentation, which in turn sheds new light on the interplay of meaning and argumentation in general.
... Stances are usually defined as text fragments representing opinions, perspectives, points of views or attitudes with respect to a target [52,53,70,89,221]. They can be expressed explicitly or implicitly [146]. Fragments can be messages such as tweets or posts [55,86], paragraphs [144] or complete articles [70]. ...
Analyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model – Open Claims – for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis.
... While massive amounts of user-generated arguments are available in various debate portals or writing platforms, these arguments are often incomplete, missing an explicitly stated conclusion or lacking essential premises. Such omissions are frequent and often due to rhetorical reasons (Rajendran et al., 2016;Becker et al., 2021). However, arguments lacking an explicit conclusion create challenges for downstream processing tasks (Opitz et al., 2021;Alshomary et al., 2020;Gurcke et al., 2021). ...
... First approaches in this direction attempt to extract missing parts by copying from similar or related arguments, or by applying common, handcrafted argument patterns (Rajendran et al., 2016;Reisert et al., 2018). Yet, these approaches are limited due to the variety of human argumentation and do not generalize well to novel topics. ...
... Boltuzic and Najder [14] investigate how to identify such implicit knowledge by analysing a large amount of text data from a variety of sources. In Habernal&Gurevych's work [14], the warrant is implicit because it is obvious from the statement's meaning, but Rajendran et al. indicated that if it is explicitly required, the argument synthesis method should be used [15]. Rajendran et al. [15] propose a method for creating a premise similar to a warrant in online review opinions that connects an aspect-related opinion to an overall opinion. ...
... In Habernal&Gurevych's work [14], the warrant is implicit because it is obvious from the statement's meaning, but Rajendran et al. indicated that if it is explicitly required, the argument synthesis method should be used [15]. Rajendran et al. [15] propose a method for creating a premise similar to a warrant in online review opinions that connects an aspect-related opinion to an overall opinion. However, their work's annotated dataset was insufficiently large to be useful for deep learning models. ...
The warrant element of the Toulmin model is critical for fact-checking and assessing the strength of an argument. As implicit information, warrants justify the arguments and explain why the evidence supports the claim. Despite the critical role warrants play in facilitating argument comprehension, the fact that most works aim to select the best warrant from existing structured data and labelled data is scarce presents a fact-checking challenge, particularly when the evidence is insufficient, or the conclusion is not inferred or generated well based on the evidence. Additionally, deep learning methods for false information detection face a significant bottleneck due to their training requirement of a large amount of labelled data. Manually annotating data, on the other hand, is a time-consuming and laborious process. Thus, we examine the extent to which warrants can be retrieved or reconfigured using unstructured data obtained from their premises.
... Boltuzic and Najder [14] investigate how to identify such implicit knowledge by analysing a large amount of text data from a variety of sources. In Habernal&Gurevych's work [14], the warrant is implicit because it is obvious from the statement's meaning, but Rajendran et al. indicated that if it is explicitly required, the argument synthesis method should be used [15]. Rajendran et al. [15] propose a method for creating a premise similar to a warrant in online review opinions that connects an aspect-related opinion to an overall opinion. ...
... In Habernal&Gurevych's work [14], the warrant is implicit because it is obvious from the statement's meaning, but Rajendran et al. indicated that if it is explicitly required, the argument synthesis method should be used [15]. Rajendran et al. [15] propose a method for creating a premise similar to a warrant in online review opinions that connects an aspect-related opinion to an overall opinion. However, their work's annotated dataset was insufficiently large to be useful for deep learning models. ...
The warrant element of the Toulmin model is critical for fact-checking and assessing the strength of an argument. As implicit information, warrants justify the arguments and explain why the evidence supports the claim. Despite the critical role warrants play in facilitating argument comprehension, the fact that most works aim to select the best warrant from existing structured data and labelled data is scarce presents a fact-checking challenge, particularly when the evidence is insufficient, or the conclusion is not inferred or generated well based on the evidence. Additionally, deep learning methods for false information detection face a significant bottleneck due to their training requirement of a large amount of labelled data. Manually annotating data, on the other hand, is a time-consuming and laborious process. Thus, we examine the extent to which warrants can be retrieved or reconfigured using unstructured data obtained from their premises.
... (Lewis et al., 2020) in three different setting for an input enthymeme from dataset by Habernal et al. (2018) The missing premise in this case is the generalization "Dogs generally bark when a person enters an area unless the dog knows the person well. " While there has been work on identification (i.e., classification) and reconstruction of implicit premises in enthymemes (Rajendran et al., 2016;Habernal et al., 2018;Reisert et al., 2015;Boltužić and Šnajder, 2016;Razuvayevskaya and Teufel, 2017), to our knowledge, automatically generating an implicit premise from a given enthymeme is a new task. There are two main challenges that need to be addressed: 1) lack of large scale data of incomplete arguments together with annotated missing premises needed to train a sequence-tosequence model (the largest such set contains 1.7K instances (Habernal et al., 2018)); and 2) the inherent need to model commonsense or word knowledge. ...
... Prior work on enthymeme reconstruction has focused primarily on the identification (i.e., classification) of implicit premises in enthymemes (Rajendran et al., 2016;Habernal et al., 2018;Reisert et al., 2015;Boltužić and Šnajder, 2016;Razuvayevskaya and Teufel, 2017). Boltužić and Šnajder (2016) study how to identify enthymemes in online discussions, while Habernal et al. (2018) present the task of identifying the correct warrant given two candidates warrants in order to reconstruct an enthymeme. ...
... Boltužić and Šnajder (2016) study how to identify enthymemes in online discussions, while Habernal et al. (2018) present the task of identifying the correct warrant given two candidates warrants in order to reconstruct an enthymeme. Rajendran et al. (2016) introduce an approach to classify the stance of a statement as implicit or explicit, as a first step towards the long term goal of enthymeme reconstruction. Unlike these works which propose discriminative approaches to identify an enthymeme or the (correct) implicit premises, we focus on generative O1 Alex had his heart set on an ivy league college ...
Enthymemes are defined as arguments where a premise or conclusion is left implicit. We tackle the task of generating the implicit premise in an enthymeme, which requires not only an understanding of the stated conclusion and premise but also additional inferences that could depend on commonsense knowledge. The largest available dataset for enthymemes (Habernal et al., 2018) consists of 1.7k samples, which is not large enough to train a neural text generation model. To address this issue, we take advantage of a similar task and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020). However, we show that simply using a state-of-the-art seq2seq model fine-tuned on this data might not generate meaningful implicit premises associated with the given enthymemes. We demonstrate that encoding discourse-aware commonsense during fine-tuning improves the quality of the generated implicit premises and outperforms all other baselines both in automatic and human evaluations on three different datasets.
... (Lewis et al., 2020) in three different setting for an input enthymeme from dataset by Habernal et al. (2018) The missing premise in this case is the generalization "Dogs generally bark when a person enters an area unless the dog knows the person well. " While there has been work on identification (i.e., classification) and reconstruction of implicit premises in enthymemes (Rajendran et al., 2016;Habernal et al., 2018;Reisert et al., 2015;Boltužić and Šnajder, 2016;Razuvayevskaya and Teufel, 2017), to our knowledge, automatically generating an implicit premise from a given enthymeme is a new task. There are two main challenges that need to be addressed: 1) lack of large scale data of incomplete arguments together with annotated missing premises needed to train a sequence-tosequence model (the largest such set contains 1.7K instances (Habernal et al., 2018)); and 2) the inherent need to model commonsense or word knowledge. ...
... Prior work on enthymeme reconstruction has focused primarily on the identification (i.e., classification) of implicit premises in enthymemes (Rajendran et al., 2016;Habernal et al., 2018;Reisert et al., 2015;Boltužić and Šnajder, 2016;Razuvayevskaya and Teufel, 2017). Boltužić and Šnajder (2016) study how to identify enthymemes in online discussions, while Habernal et al. (2018) present the task of identifying the correct warrant given two candidates warrants in order to reconstruct an enthymeme. ...
... Boltužić and Šnajder (2016) study how to identify enthymemes in online discussions, while Habernal et al. (2018) present the task of identifying the correct warrant given two candidates warrants in order to reconstruct an enthymeme. Rajendran et al. (2016) introduce an approach to classify the stance of a statement as implicit or explicit, as a first step towards the long term goal of enthymeme reconstruction. Unlike these works which propose discriminative approaches to identify an enthymeme or the (correct) implicit premises, we focus on generative O1 Alex had his heart set on an ivy league college ...
... While such information can be easily filled in by the hearer, a computational system typically does not possess the knowledge that is needed to reconstruct the implied information. Especially in argumentative texts it is very common that premises are implied and omitted [4,22,43]. These arguments are called enthymemes. ...
Most information we consume as a society is obtained over the Web. News – often from questionable sources – are spread online, as are election campaigns; calls for (collective) action spread with unforeseen speed and intensity. All such actions have argumentation at their core, and the conveyed content is often strategically selected or rhetorically framed. The responsibility of critical analysis of arguments is thus tacitly transferred to the content consumer who is often not prepared for the task, nor aware of the responsibility.
The ExpLAIN project aims at making the structure and reasoning of arguments explicit – not only for humans, but for Robust Argumentation Machines that are endowed with language understanding capacity. Our vision is a system that is able to deeply analyze argumentative text: that identifies arguments and counter-arguments, and reveals their internal structure, conveyed content and reasoning. A particular challenge for such a system is to uncover implicit knowledge which many arguments rely on. This requires human background knowledge and reasoning capacity, in order to explicate the complete reasoning of an argument.
This article presents ongoing research of the ExpLAIN project that aims to make the vision of such a system a tangible aim. We introduce the problems and challenges we need to address, and present the progress we achieved until now by applying advanced natural language and knowledge processing methods. Our approach puts particular focus on leveraging available sources of structured and unstructured background knowledge, the automatic extension of such knowledge, the uncovering of implicit content, and reasoning techniques suitable for informal, everyday argumentation.
... Motivated by the importance of finding the thesis, Boltuzic and Šnajder (2016) study how to identify such enthymemes given the other components. Similarly, Habernal et al. (2018) present the task of identifying the correct warrant from two options, and Rajendran et al. (2016) aim to generate the premise connecting an aspect-related opinion to an overall opinion. Instead of missing premises, we aim to synthesize (parts of) an argument's conclusion. ...
... However, they focus on non-ideological topics (usually products, e.g., iPhone vs. Galaxy). In contrast, we target ideological topics (e.g., Gay Marriage, Abortion) whose stance is harder to detect due to less fre-quent use of sentiment words and a wider variety of brought up issues and arguments (Rajendran et al., 2016;Wang et al., 2019). On the one hand, these works extract topic aspects (e.g., screen resolution, battery) and polarities towards these aspects, a step that is unfeasible for ideological topics. ...
