Clippers 10/26: Sara Court on Maltese Plural Inflection Class Structure

Modeling Plural Inflection Class Structure in Maltese

Theoretical and typological research in morphology define an inflectional paradigm as the collection of related word forms associated with a given lexeme. When multiple lexemes share the same paradigm, they in turn define an inflection class. Recent work in morphology uses information theory to quantify the complexity of a language’s inflectional system in terms of interpredictability across word forms and paradigms. These studies provide precise synchronic descriptions of inflectional structure, but are unable to account for how or why these systems emerge in language-specific ways. I’ll be presenting on ongoing research for my QP1 that addresses this question by modeling the relative influence of three factors – phonological form, semantic meaning, and etymological origin – on the organization of plural inflection classes in Maltese.

Clippers 10/19: William Schuler on the reality of syntactic categories

Are syntactic categories real?

People can express novel, precise complex ideas — plans with sophisticated contingencies, predictive models of interrelated uncertain events, and more — which seems to suggest a formal, compositional semantics in which sentences are divided into categories with associated semantic functions. But state-of-the-art NLP systems – transformers like BERT and GPT-3 — don’t work like that. This talk will review evidence about syntactic categories from sentence processing experiments and grammar inductions simulations conducted over the past few years in the OSU computational cognitive modeling lab, and hazard some guesses about the cognitive status of syntactic categories.

Clippers 10/12: Shuaichen Chang on semi-supervised heterogeneous feature learning in a large-scale conversational AI system

Title: Semi-Supervised Heterogeneous Feature Learning in a Large-Scale Conversational AI System

Abstract: This paper aims to improve an important downstream component of a large-scale industrial conversational AI system. The component is called the Skill Routing Component (SRC) and is responsible for a variety of tasks. As the last component before executing user requests, SRC utilizes many textual and symbolic features obtained from heterogeneous upstream components like automatic speech recognition (ASR) and natural language understanding (NLU), which necessitates the need for an efficient way to utilize these features. To achieve this, we propose a unified transformer model which in contrast to the traditional methods encodes the heterogeneous features into a shared latent space. Next, there is an inherent connection between SRC tasks and upstream NLU tasks. We utilize noisy NLU data for pre-training the unified SRC model via specifically curated objectives and fine-tune it separately on the different SRC tasks. Our method shows an average improvement of 1.8% on four SRC tasks over the state-of-the-art baseline.

Clippers 10/5: Vishal Sunder on end-to-end dialog history integration for SLU

Title: Towards end-to-end integration of dialog history for improved spoken language understanding.

Abstract: Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in text form, which makes the model dependent on a cascaded automatic speech recognizer (ASR). This rescinds the benefits of an E2E system which is intended to be compact and robust to ASR errors. In this work, we propose a hierarchical conversation model that is capable of directly using dialog history in speech form, making it fully E2E. We also distill semantic knowledge from the available gold conversation transcripts by jointly training a similar text-based conversation model with an explicit tying of acoustic and semantic embeddings. We also propose a novel technique that we call DropFrame to deal with the long training time incurred by adding dialog history in an E2E manner. On the HarperValleyBank dialog dataset, our E2E history integration outperforms a history independent baseline by 7.7% absolute F1 score on the task of dialog action recognition. Our model performs competitively with the state-of-the-art history based cascaded baseline, but uses 48% fewer parameters. In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.