We consider the task of generating structured representations of text using large language models (LLMs).We focus on tables and mind maps as representative modalities.Tables are more organized way of representing data, while mind maps provide a visually dynamic and flexible approach, particularly suitable for sparse content.Despite the effectiveness of LLMs on different tasks, we show that current models struggle with generating structured outputs.In response, we present effective prompting strategies for both of these tasks.We introduce a taxonomy of problems around factuality, global and local structure, common to both modalities and propose a set of critiques to tackle these issues resulting in an absolute improvement in accuracy of +37pp (79%) for mind maps and +15pp (78%) for tables.To evaluate semantic coverage of generated structured representations we propose AUTO-QA, and we verify the adequacy of AUTO-QA using SQuAD dataset.We further evaluate the usefulness of structured representations via a text comprehension user study.The results show a significant reduction in comprehension time compared to text when using table (42.9%) and mind map (31.9%), without loss in accuracy.

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).To this end, we develop a dataset where user questions are annotated with SPARQL parses and system answers correspond to execution results thereof.We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time.We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. 1

In this paper we consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types.We focus on models which are capable of interactively mapping user utterances into executable logical forms (e.g., SPARQL) in the context of the conversational history.Our key idea is to represent information about an utterance and its context via a subgraph which is created dynamically, i.e., the number of nodes varies per utterance.Rather than treating the subgraph as a sequence, we exploit its underlying structure and encode it with a graph neural network which further allows us to represent a large number of (unseen) nodes.Experimental results show that dynamic context modeling is superior to static approaches, delivering performance improvements across the board (i.e., for simple and complex questions).Our results further confirm that modeling the structure of context is better at processing discourse information, (i.e., at handling ellipsis and resolving coreference) and longer interactions.

In this innovative practice full paper, we investigate the benefits of using an augmented reality (AR) application in wireless network security education. For this purpose, we design and implement an AR application which passively captures wireless network traffic and superimposes the otherwise imperceptible network packets on the camera image of sending and receiving devices. We perform a user-study with 25 participants, who are provided with a laptop which runs our application. Students are tasked to spot Evil Twin, Address-Resolution Protocol (ARP) Poisoning and Denial of Service (DoS) attacks launched on a 802.11 Wi-Fi network by observing it with the AR application. We find that students were able to spot and describe those attacks intuitively. From those results we conclude that an AR-based approach is a valuable addition to wireless network security laboratory courses. Additionally, our results indicate, that our application provides access to this complex topic even for users without a technical background. To the best of our knowledge, this is the first experimental study, which shows the beneficial effects of using AR in wireless network security education.

Abstract We present a memory-based model for context- dependent semantic parsing. Previous approaches focus on enabling the decoder to copy or modify the parse from the previous utterance, assuming there is a dependency between the current and previous parses. In this work, we propose to represent contextual information using an external memory. We learn a context memory controller that manages the memory by maintaining the cumulative meaning of sequential user utterances. We evaluate our approach on three semantic parsing benchmarks. Experimental results show that our model can better process context-dependent information and demonstrates improved performance without using task-specific decoders.

We propose a novel framework for controllable natural language transformation. Realizing that the requirement of parallel corpus is practically unsustainable for controllable generation tasks, an unsupervised training scheme is introduced. The crux of the framework is a deep neural encoder-decoder that is reinforced with text-transformation knowledge through auxiliary modules (called scorers). These scorers, based on off-the-shelf language processing tools, decide the learning scheme of the encoder-decoder based on its actions. We apply this framework for the text-transformation task of formalizing an input text by improving its readability grade; the degree of required formalization can be controlled by the user at run-time. Experiments on public datasets demonstrate the efficacy of our model towards: (a) transforming a given text to a more formal style, and (b) varying the amount of formalness in the output text based on the specified input control. Our code and datasets are released for academic use.

Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.However, the fundamental challenge lies in obtaining high-quality annotations of (utterance, program) pairs across various domains needed for training such models.To overcome this, we propose a novel framework to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations).Weakly supervised training is particularly arduous as the program search space grows exponentially in a multi-domain setting.To solve this, we incorporate a multipolicy distillation mechanism in which we first train domain-specific semantic parsers (teachers) using weak supervision in the absence of the ground truth programs, followed by training a single unified parser (student) from the domain specific policies obtained from these teachers.The resultant semantic parser is not only compact but also generalizes better, and generates more accurate programs.It further does not require the user to provide a domain label while querying.On the standard OVERNIGHT dataset (containing multiple domains), we demonstrate that the proposed model improves performance by 20% in terms of denotation accuracy in comparison to baseline techniques.

The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.The core framework is composed of a shared encoder and a pair of attentional-decoders, crucially assisted by discrimination-based losses and denoising.The framework is trained using unlabeled text collected from en-Wikipedia dump.Our analysis (both quantitative and qualitative involving human evaluators) on public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods.It also outperforms viable unsupervised baselines.Adding a few labeled pairs helps improve the performance further.

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.

Preksha Nema, Shreyas Shetty, Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Mitesh M. Khapra. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

The ability to create and interact with large-scale domain-specific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.