Fall 2022 Schedule

Location: Hybrid (starting Feb 23) - Fleming 279, and the zoom link below

Time: Wednesdays at 10:30am, Mountain Time

Zoom link: https://cuboulder.zoom.us/j/97014876908

Date	Title
1.12.22	Planning, introductions, welcome! CompSem meetings will be virtual until further notice (https://cuboulder.zoom.us/j/97014876908)
01.19.22	Kai Larsen, CU Boulder Leeds School of Business Validity in Design Research Research in design science has always recognized the importance of evaluating its knowledge outcomes, particularly of assessing the efficacy, utility, and attributes of the artifacts produced (e.g., A.I. systems, machine learning models, theories, frameworks). However, demonstrating the validity of design science research (DSR) is challenging and not well understood. This paper defines DSR validity and proposes a DSR Validity Framework. We evaluate the framework by assembling and analyzing an extensive data set of research validities papers from various disciplines, including design science. We then analyze the use of validity concepts in DSR and validate the framework. The results demonstrate that the DSR Validity Framework may be used to guide how validity can, and should, be used as an integral aspect of design science research. We further describe the steps for selecting appropriate validities for projects and formulate efficacy validity and characteristic validity claims suitable for inclusion in manuscripts. Keywords: Design science research (DSR), research validity, validity framework, artifact, evaluation, efficacy validity, characteristic validity.
01.26.22	Elizabeth Spaulding, prelim Prelim topic: Evaluation for Abstract Meaning Representations Abstract Meaning Representation (AMR) is a semantic representation language that provides a way to represent the meaning of a sentence in the form of a graph. The task of AMR parsing—automatically extracting AMR graphs from natural language text—necessitates evaluation metrics to develop neural parsers. My prelim is a review of AMR evaluation metrics and the strengths and weaknesses of each approach, as well as a discussion of gaps and unexplored questions in the current literature.
02.02.22	NO MEETING
02.09.22	SCiL live session!
02.16.22	NO MEETING
02.23.22	CompSem meetings go back to being hybrid! (Fleming 279 or https://cuboulder.zoom.us/j/97014876908) Invited talk: Aniello de Santo, University of Utah Bridging Typology and Learnability via Formal Language Theory The complexity of linguistic patterns is object of extensive debate in research programs focused on probing the inherent structure of human language abilities. But in what sense is a linguistic phenomenon more complex than another, and what can complexity tell us about the connection between linguistic typology and human cognition? In this talk, I overview a line of research approaching these questions from the perspective of recent advances in formal language theory. I will first broadly discuss how language theoretical characterizations allow us to focus on essential properties of linguistic patterns under study. I will emphasize how typological insights can help us refine existing mathematical characterizations, arguing for a two-way bridge between disciplines, and show how the theoretical predictions made by logic/algebraic formalization of typological generalizations can be used to test learning biases in humans (and machines). In doing so, I aim to illustrate the relevance of mathematically grounded approaches to cognitive investigations into linguistic generalizations, and thus further fruitful cross-disciplinary collaborations. Bio Sketch: Aniello De Santo is an Assistant Professor in the Linguistics Department at the University of Utah. Before joining Utah, he received a PhD in Linguistics from Stony Brook University. His research broadly lies at the intersection between computational, theoretical, and experimental linguistics. He is particularly interested in investigating how linguistic representations interact with general cognitive processes, with particular focus on sentence processing and learnability. In his past work, he has mostly made use of symbolic approaches grounded in formal language theory and rich grammar formalisms (Minimalist Grammars, Tree Adjoining Grammars).
03.02.22	Kevin Cohen, Computational Bioscience Program, U. Colorado School of Medicine Chalk talk: Studying the science in biomedical natural language processing At this CompSem meeting, I will give a talk chalk on a grant proposal that I am preparing. "Chalk talks" are a kind of presentation that you will have to do when you hit the job market, and once you've found that wonderful job, they may be a regular part of your faculty responsibilities. I will begin with an introduction to the form and functions of this kind of talk, go over the review criteria for the kind of grant for which I am applying, and then give a chalk talk on my proposal. Please come ready to critique it harshly--my grandmother will tell me how great it is.
03.09.22	Ghazaleh Kazeminejad, proposal defense NOTE: Special start time 10am Topic: Neural-Symbolic NLP: exploiting computational lexical resources Recent major advances in Natural Language Processing (NLP) have relied on a distributional approach, representing language numerically to enable complex mathematical operations and algorithms. These numeric representations have been based on the probabilistic distributions of linguistic units. The main recent breakthrough in NLP has been the result of feeding massive data to the machine and using neural network architectures, allowing the machine to learn a model that approximates a given language (grammar and lexicon). Following this paradigm shift, NLP researchers introduced transfer learning, enabling researchers with less powerful computational resources to use their pre-trained language models and transfer what the machine has learned to a new downstream NLP task. However, there are some NLP tasks, particularly in the realm of Natural Language Understanding (NLU), where surface level representations and purely statistical models may benefit from symbolic knowledge and deeper level representations. In this work, we explore contributions that symbolic computational lexical resources can still make to system performances on two different tasks. In particular, we propose to expose the model to symbolic knowledge, including external world knowledge (e.g typical features of entities such as their typical functions or whereabouts) as well as linguistic knowledge (e.g. syntactic dependencies and semantic relationships among the constituents). One of our goals for this work is finding an appropriate numeric representation for this type of symbolic knowledge. We propose to utilize the semantic predicates from VerbNet, semantic roles from VerbNet and PropBank, syntactic dependency labels, and world knowledge from ConceptNet as symbolic knowledge, going beyond the types of symbolic knowledge used so far in neural-symbolic approaches. We will expose a pre-trained language model to symbolic knowledge in two ways. First, we will embed these relations into a neural network architecture by modifying the input representations. Second, we will treat the knowledge as constraints on the output, penalizing the model at the end of each training step if the constraints are not met in the model predictions at that step. To evaluate this approach, we propose to test it on two downstream NLP tasks: Event Extraction and Entity State Tracking. We propose a thorough investigation of the two tasks, particularly focusing on where they have benefitted from a neural-symbolic approach, and whether and how we could further improve the performance on these tasks by introducing both linguistic and world knowledge to the model.
03.16.22	Chelsea Chandler, thesis defense NOTE: Special start time 10am Title: Methods for Multimodal Assessment of Cognitive and Mental State Barriers to healthcare access such as time, affordability, and stigma are common in patients suffering from psychiatric and neurodegenerative disorders. Psychiatric patients often need to be monitored with frequent clinical interviews to avoid costly emergency care and preventable events. However, there simply are not enough clinicians to monitor these patients on a regular basis and infrequent clinical evaluations may result in missing the subtle changes in patient state that occur over time. For those suffering from neurodegenerative disorders, the traditional approaches to detecting early onset lack the sensitivity needed to catch the subtle signs of cognitive decline. In order to move toward a more standardized, consistent, and reliable assessment and diagnosis process, machine learning and natural language processing methods can be harnessed to create accurate and accessible prognostic systems that could help to alleviate the burden of mental disorders in society. In this dissertation, a multidisciplinary set of methodologies for the automated assessment of psychiatric mental state were developed that were sufficiently accurate and explainable to nurture trust from patients and clinicians, and also longitudinal and multimodal to model the dynamic and multifaceted nature of mental disorders. The viability of an automated assessment pipeline was examined: from the administration of neuropsychological tests and transcription of spoken responses, to the extraction of construct-relevant data features and prediction of psychiatric mental states. A similar approach was taken for the screening of the neurodegenerative disorders Alzheimer’s disease and Mild Cognitive Impairment. Implications for the real world use of multimodal machine learning for mental disorders are discussed, providing a crucial step towards clinical translation and implementation. Biographical Note Chelsea Chandler is a joint PhD candidate in the Department of Computer Science and Institute of Cognitive Science and is advised by Peter Foltz and Jim Martin. Previous to her PhD, she received a BA in Mathematics and Computer Science from the University of Virginia, worked as a software engineer for Lockheed Martin, and received a MS in Computer Science from the University of Colorado Boulder.
03.23.22	***Spring Break***
03.30.22	CLASIC Open House
04.06.22	Grad student appreciation lunch!!!
04.13.22	*** No meeting ***
04.20.22	Sagi Shaier, prelim Prelim topic: Knowledge Incorporation In Dialogue Systems Current dialogue systems are capable of communicating reasonably well using day-to-day language. However, the generated dialogue often appears as uninformative. Additionally, depending on the use case of the system, one may want to make it more empathetic, have an enhanced medical or soccer knowledge, or even have an improved common sense. One approach to tackle such challenges is to incorporate knowledge in various forms (e.g., emotions, facts, complete documents) into such systems. In this talk, I will present current and past research of such methods, and discuss them from the perspective of human memory.
04.27.22 05.04.22	Adam Wiemerslage, prelim NOTE: Special date

Past Schedules

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