| Challenge: | Existing approaches to explain the correct answer in multiple-choice QA are low in F-scores and lack of performance. |
| Approach: | They introduce a lightweight focus feature in a transformer-based NLP model and examine performance improvements. |
| Outcome: | The proposed model achieves the highest scores, second only to a computationally intensive system. |
Similar Papers
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. |
Paraphrase Identification via Textual Inference (2024.starsem-1)
Copied to clipboard
| Challenge: | Paraphrase identification (PI) and natural language inference (NLI) are important tasks in natural language processing. |
| Approach: | They propose a method for paraphrase identification and natural language inference using an NLI system to solve these tasks. |
| Outcome: | The proposed method outperforms dedicated PI models on PI datasets and provides insights into limitations of current benchmarks. |
Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking (2022.starsem-1)
Copied to clipboard
Ronen Tamari, Kyle Richardson, Noam Kahlon, Aviad Sar-shalom, Nelson F. Liu, Reut Tsarfaty, Dafna Shahaf
| Challenge: | Controlled synthetic tasks are an important resource for diagnosing model behavior. |
| Approach: | They propose a framework that provides fine-grained control over task generation in bAbI. |
| Outcome: | The proposed framework provides fine-grained control over task generation in the bAbI benchmark. |
Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens (2023.starsem-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence paraphrase generation models struggle with the generation of diverse paraphrases. |
| Approach: | They propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
| Outcome: | The proposed model learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT (2020.starsem-1)
Copied to clipboard
| Challenge: | Existing studies have found that BERT can correctly retrieve noun hypernyms in cloze tasks, but this does not correspond to systematic knowledge in BERT. |
| Approach: | They propose to use BERT to probe for hypernymy knowledge encoded in representations for cloze tasks to find out whether it is systematic or not . |
| Outcome: | The proposed model can retrieve hypernyms in cloze tasks, but not systematic knowledge in BERT. |
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking (2020.starsem-1)
Copied to clipboard
| Challenge: | Existing methods for dialog state tracking are ontology-based and ontologie-free . however, it is not clear enough which slots are better handled by either of the two methods . |
| Approach: | They propose a dual-strategy model that integrates both ontology-based and ontological-free methods. |
| Outcome: | The proposed model outperforms the existing model on noisy and cleaner datasets. |
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. |
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. |
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021.starsem-1)
Copied to clipboard
Paolo Pedinotti, Giulia Rambelli, Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache
| Challenge: | Prior work has explored the ability of computational models to predict word semantic fit with a given predicate. |
| Approach: | They compare Transformers Language Models to SDM to assess their performance . they found that TLMs do not capture important aspects of event knowledge . people can discriminate between typical and atypical events, they say . |
| Outcome: | The proposed models can achieve comparable performance to SDM, but they lack important aspects of event knowledge. |
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. |