This research explores integration of a Large Language Model (LLM) fine-tuned to conversationally control the user interface (UI) for a Semantic Automation Layer (SAL). We condense SAL capabilities from prior work and prioritize with business analysts and data engineers via a Kano model, before implementing a prototypical UI. We augment the UI with our conversational engine and propose In-situ Prompt Engineering and learn from Human Feedback to smoothen the interaction and manipulation of UI through natural language commands. To evaluate the efficacy and usability of conversational control in various use-case scenarios, we conduct and report on an empirical interaction design user study. Our findings provide evidence supporting enhanced user engagement and satisfaction. We also observe significant increase of trust in AI after working with our conversational UI. This work generates areas for further refinement and research towards more intelligent, highly-integrated conversational UIs even beyond our application within Semantic Automation. We discuss our findings and point out next steps paving the way for future research and development in creating more intuitive and adaptive user interfaces.

We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata. We address this problem as a sequence generation task by leveraging pre-trained sequence-to-sequence language models, e.g. BART. Given a sentence, we fine-tune such models to detect pairs of entity mentions and jointly generate a set of facts consisting of the full set of semantic annotations for a KG, such as entity labels, entity types, and their relationships. To showcase the capabilities of our tool, we build a web application consisting of a set of UI widgets that help users to navigate through the semantic data extracted from a given input text. We make the KnowGL model available at https://huggingface.co/ibm/knowgl-large.

Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, Alfio Gliozzo. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.One way to make headway for this problem is through advancements in a related task known as slot filling.In this task, given an entity query in form of [ENTITY, SLOT, ?], a system is asked to 'fill' the slot by generating or extracting the missing value exploiting evidence extracted from relevant passage(s) in the given document collection.The recent works in the field try to solve this task in an end-to-end fashion using retrieval-based language models.In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models.Our model reports large improvements on both T-REx and zsRE slot filling datasets, improving both passage retrieval and slot value generation, and ranking at the top-1 position in the KILT leaderboard.Moreover, we demonstrate the robustness of our system showing its domain adaptation capability on a new variant of the TACRED dataset for slot filling, through a combination of zero/few-shot learning.We release the source code and pre-trained models 1 .

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.

Extracting lexico-semantic relations as graphstructured taxonomies, also known as taxonomy construction, has been beneficial in a variety of NLP applications.Recently Graph Neural Network (GNN) has shown to be powerful in successfully tackling many tasks.However, there has been no attempt to exploit GNN to create taxonomies.In this paper, we propose Graph2Taxo, a GNN-based cross-domain transfer framework for the taxonomy construction task.Our main contribution is to learn the latent features of taxonomy construction from existing domains to guide the structure learning of an unseen domain.We also propose a novel method of directed acyclic graph (DAG) generation for taxonomy construction.Specifically, our proposed Graph2Taxo uses a noisy graph constructed from automatically extracted noisy hyponym-hypernym candidate pairs, and a set of taxonomies for some known domains for training.The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain.Experiments on benchmark datasets from science and environment domains show that our approach attains significant improvements correspondingly over the state of the art.