DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models (2022.starsem-1)
Copied to clipboard
| Challenge: | ArgumentAnalyst is a multi-dimensional, modular framework for performing deep argument analysis using existing pre-trained language models (PTLMs). |
| Approach: | They propose a multi-dimensional, modular framework for performing deep argument analysis using current pre-trained language models (PTLMs) ArgumentAnalyst reconstructs argumentative texts as valid arguments by inserting missing premises and conclusions, formalizing inferences, and coherently linking the reconstruction to the source text. |
| Outcome: | The proposed framework can replicate established heuristics, cope with plurality of correct solutions, and exploit higher-order evidence. |
Similar Papers
Exploring Factual Entailment with NLI: A News Media Study (2024.starsem-1)
Copied to clipboard
| Challenge: | Recent studies have focused on the relationship between factuality and Natural Language Inference (NLI). |
| Approach: | They propose a novel annotation scheme that models factual rather than textual entailment and use it to annotate a dataset of naturally occurring sentences from news articles. |
| Outcome: | The proposed annotation scheme can be used to model factual relationships on a dataset of naturally occurring sentences from news articles. |
Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis (2021.starsem-1)
Copied to clipboard
| Challenge: | Compchains are a categorization of the hierarchy of predicate dependency relations present within a UD parse. |
| Approach: | They introduce compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse. |
| Outcome: | The proposed model performs poorly on sentences with predicate-argument structure with more than one level of embedding. |
BiQuAD: Towards QA based on deeper text understanding (2021.starsem-1)
Copied to clipboard
| Challenge: | Recent question answering and machine reading benchmarks require systems to pinpoint the span of the answer to a given text. |
| Approach: | They propose a dataset that requires deeper comprehension to answer questions extractively and deductively. |
| Outcome: | The proposed dataset outperforms existing benchmarks on extractive and deductive questions. |
When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs) (2023.starsem-1)
Copied to clipboard
| Challenge: | In this paper, we examine the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, and sentence fragments for natural language inference (NLI). |
| Approach: | They propose to fine-tune large language models to accommodate different sentence types for natural language inference (NLI) they also explore ChatGPT's concept of entailment by using a symbolic semantic parser. |
| Outcome: | The proposed models can accommodate different sentence types without losing too much accuracy on MNLI-matched models. |
Reading Comprehension as Natural Language Inference:A Semantic Analysis (2020.starsem-1)
Copied to clipboard
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
| Challenge: | In recent past, Natural language Inference (NLI) has gained significant attention, but its true impact has not been well studied. |
| Approach: | They propose to transform a large RACE dataset into an NLI model and compare it to a state-of-the-art model. |
| Outcome: | The proposed model outperforms the previous model on a question-answer concatenation form and a coherent entailment form. |
Compositional Structured Explanation Generation with Dynamic Modularized Reasoning (2024.starsem-1)
Copied to clipboard
| Challenge: | Large-scale language models have shown remarkable performance on reasoning tasks such as reading comprehension, natural language inference, story generation, etc. |
| Approach: | They propose a compositional structured explanation generation task to test a model's ability to generalize from generating entailment trees to more steps, focusing on the length and shapes of engorgement trees. |
| Outcome: | The proposed model shows competitive compositional generalization abilities in a generation setting. |
A Generative Approach for Mitigating Structural Biases in Natural Language Inference (2022.starsem-1)
Copied to clipboard
| Challenge: | Natural language inference datasets contain artifacts and biases that allow models to perform poorly by using a biased subset of the input without considering the remainder features. |
| Approach: | They reformulate a natural language inference task as a generative task . they find that this approach is highly robust to large amounts of bias . |
| Outcome: | The proposed model is highly robust to large amounts of bias. |
What do Large Language Models Learn about Scripts? (2022.starsem-1)
Copied to clipboard
| Challenge: | Script Knowledge is important for language understanding but expensive to produce manually and difficult to induce from text due to reporting bias. |
| Approach: | They propose a pipeline-based script induction framework which can generate good quality ESDs for unseen scenarios. |
| Outcome: | The proposed framework produces good quality ESDs for unseen scenarios, but manual evaluation shows there is room for improvement. |
Spurious Correlations in Cross-Topic Argument Mining (2021.starsem-1)
Copied to clipboard
| Challenge: | Recent work in cross-topic argument mining attempts to learn models that generalise across topics rather than relying on within-topic spurious correlations. |
| Approach: | They propose to use linear approximations of decision boundaries and manual feature grouping to learn models that generalise across topics rather than relying on within-topic spurious correlations. |
| Outcome: | The proposed model generalise across topics rather than relying on spurious correlations. |
Capturing the Content of a Document through Complex Event Identification (2022.starsem-1)
Copied to clipboard
| Challenge: | Recent work grouped granular events into more general events, called complex events . however, this approach assumes that a given complex event is always described in consecutive sentences . |
| Approach: | They propose a context-augmented representation learning approach that uses contextual information to model pairwise relation between granular events. |
| Outcome: | The proposed approach outperforms baselines on the complex event identification task. |