Article | June 10, 2021
We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum.
Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake.
Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions.
Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation.
Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization.
A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction.
Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors.
Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt.
The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation.
Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.
Article | May 3, 2021
Clear conceptualization, taxonomies, categories, criteria, properties when solving complex real-life contextualized problems is non-negotiable, a “must” to unveil the hidden potential of NPL impacting on the transparency of a model.
It is common knowledge that many authors and researchers in the field of natural language processing (NLP) and machine learning (ML) are prone to use explainability and interpretability interchangeably, which from the start constitutes a fallacy. They do not mean the same, even when looking for a definition from different perspectives.
A formal definition of what explanation, explainable, explainability mean can be traced to social science, psychology, hermeneutics, philosophy, physics and biology. In The Nature of Explanation, Craik (1967:7) states that “explanations are not purely subjective things; they win general approval or have to be withdrawn in the face of evidence or criticism.” Moreover, the power of explanation means the power of insight and anticipation and why one explanation is satisfactory involves a prior question why any explanation at all should be satisfactory or in machine learning terminology how a model is performant in different contextual situations. Besides its utilitarian value, that impulse to resolve a problem whether or not (in the end) there is a practical application and which will be verified or disapproved in the course of time, explanations should be “meaningful”.
We come across explanations every day. Perhaps the most common are reason-giving ones. Before advancing in the realm of ExNLP, it is crucial to conceptualize what constitutes an explanation. Miller (2017) considered explanations as “social interactions between the explainer and explainee”, therefore the social context has a significant impact in the actual content of an explanation. Explanations in general terms, seek to answer the why type of question. There is a need for justification. According to Bengtsson (2003) “we will accept an explanation when we feel satisfied that the explanans reaches what we already hold to be true of the explanandum”, (being the explanandum a statement that describes the phenomenon to be explained (it is a description, not the phenomenon itself) and the explanan at least two sets of statements, used for the purpose of elucidating the phenomenon).
In discourse theory (my approach), it is important to highlight that there is a correlation between understanding and explanation, first and foremost. Both are articulated although they belong to different paradigmatic fields. This dichotomous pair is perceived as a duality, which represents an irreducible form of intelligibility.
When there are observable external facts subject to empirical validation, systematicity, subordination to hypothetic procedures then we can say that we explain. An explanation is inscribed in the analytical domain, the realm of rules, laws and structures. When we explain we display propositions and meaning. But we do not explain in a vacuum. The contextual situation permeates the content of an explanation, in other words, explanation is an epistemic activity: it can only relate things described or conceptualized in a certain way. Explanations are answers to questions in the form: why fact, which most authors agree upon.
Understanding can mean a number of things in different contexts. According to Ricoeur “understanding precedes, accompanies and swathes an explanation, and an explanation analytically develops understanding.” Following this line of thought, when we understand we grasp or perceive the chain of partial senses as a whole in a single act of synthesis. Originally, belonging to the field of the so-called human science, then, understanding refers to a circular process and it is directed to the intentional unit of discourse whereas an explanation is oriented to the analytical structure of a discourse.
Now, to ground any discussion on what interpretation is, it is crucial to highlight that the concept of interpretation opposes the concept of explanation. They cannot be used interchangeably. If considered as a unit, they composed what is called une combinaison éprouvé (a contrasted dichotomy). Besides, in dissecting both definitions we will see that the agent that performs the explanation differs from the one that produce the interpretation.
At present there is a challenge of defining—and evaluating—what constitutes a quality interpretation. Linguistically speaking, “interpretation” is the complete process that encompasses understanding and explanation. It is true that there is more than one way to interprete an explanation (and then, an explanation of a prediction) but it is also true that there is a limited number of possible explanations if not a unique one since they are contextualized. And it is also true that an interpretation must not only be plausible, but more plausible than another interpretation. Of course there are certain criteria to solve this conflict. And to prove that an interpretation is more plausible based on an explanation or the knowledge could be related to the logic of validation rather than to the logic of subjective probability.
Narrowing it down
How are these concepts transferred from theory to praxis? What is the importance of the "interpretability" of an explainable model? What do we call a "good" explainable model? What constitutes a "good explanation"? These are some of the many questions that researchers from both academia and industry are still trying to answer.
In the realm on machine learning current approaches conceptualize interpretation in a rather ad-hoc manner, motivated by practical use cases and applications. Some suggest model interpretability as a remedy, but only a few are able to articulate precisely what interpretability means or why it is important. Hence more, most in the research community and industry use this term as synonym of explainability, which is certainly not. They are not overlapping terms. Needless to say, in most cases technical descriptions of interpretable models are diverse and occasionally discordant.
A model is better interpretable than another model if its decisions are easier for a human to comprehend than decisions from the other model (Molnar, 2021). For a model to be interpretable (being interpretable the quality of the model), the information conferred by an interpretation may be useful. Thus, one purpose of interpretations may be to convey useful information of any kind. In Molnar’s words the higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made.” I will make an observation here and add “the higher the interpretability of an explainable machine learning model”. Luo et. al. (2021) defines “interpretability as ‘the ability [of a model] to explain or to present [its predictions] in understandable terms to a human.” Notice that in this definition the author includes “understanding” as part of the definition, giving the idea of completeness. Thus, the triadic closure explanation-understanding-interpretation is fulfilled, in which the explainer and interpretant (the agents) belong to different instances and where interpretation allows the extraction and formation of additional knowledge captured by the explainable model.
Now are the models inherently interpretable? Well, it is more a matter of selecting the methods of achieving interpretability: by (a) interpreting existing models via post-hoc techniques, or (b) designing inherently interpretable models, which claim to provide more faithful interpretations than post-hoc interpretation of blackbox models. The difference also lies in the agency –like I said before– , and how in one case interpretation may affect the explanation process, that is model’s inner working or just include natural language explanations of learned representations or models.
Article | April 3, 2020
Primarily,the IoT stack is going beyond merely ingesting data to data analytics and management, with a focus on real-time analysis and autonomous AI capacities. Enterprises are finding more advanced ways to apply IoT for better and more profitable outcomes. IoT platforms have evolved to use standard open-source protocols and components. Now enterprises are primarily focusing on resolving business problems such as predictive maintenance or usage of smart devices to streamline business operations.Platforms focus on similar things, but early attempts at the creation of highly discrete solutions around specific use cases in place of broad platforms, have been successful. That means more vendors offer more choices for customers, to broaden the chances for success. Clearly, IoT platforms actually sit at the heart of value creation in the IoT.
Article | June 9, 2021
In recent years, we have seen more industries adopt data analytics as they realize how important it is. Even the hotel industry is not left behind in this.
This is because the hospitality industry is data-rich. And the key to maintaining a competitive advantage has come down to ‘how hotels manage and analyze this data’.
With the changes taking place in the hospitality industry, data analysis can help you gain meaningful insights that can redefine the way hotels conduct business.