Papers by Rahul Aralikatte
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs (2024.findings-naacl)
Copied to clipboard
Victor Carbune, Hassan Mansoor, Fangyu Liu, Rahul Aralikatte, Gilles Baechler, Jindong Chen, Abhanshu Sharma
| Challenge: | Visual language models (VLMs) are achieving increasingly strong performance on multimodal tasks. |
| Approach: | They propose to transfer reasoning capabilities from large-language models to VLMs by constructing a 20x larger dataset and a larger dataset to improve general reasoning capabilities. |
| Outcome: | The proposed model outperforms larger models without an upstream OCR system while keeping inference time constant. |
Compositional Generalization in Multilingual Semantic Parsing over Wikidata (2022.tacl-1)
Copied to clipboard
| Challenge: | Semantic parsers are mostly designed for and evaluated on English resources, such as CFQ. |
| Approach: | They propose a method for creating a multilingual, parallel question-query dataset . they analyze compositional generalization of parsers in Hebrew, Kannada, Chinese, and English . |
| Outcome: | The proposed method analyzes compositional generalization of parsers in Hebrew, Kannada, Chinese, and English. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
Copied to clipboard
Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |
Model-based Annotation of Coreference (2020.lrec-1)
Copied to clipboard
| Challenge: | Annotators are asked to annotate coreferent spans of text, which is unnatural . we present an alternative in which annotators can preprocess documents and assign pronouns to entities. |
| Approach: | They propose an alternative in which annotators are asked to assign pronouns to entities and preprocess documents to create a knowledge base. |
| Outcome: | The proposed model-based approach leads to faster annotation and higher inter-annotator agreement and opens up an alternative approach to coreference resolution. |
Rewarding Coreference Resolvers for Being Consistent with World Knowledge (D19-1)
Copied to clipboard
Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
| Challenge: | Unresolved coreference is a bottleneck for relation extraction systems . a state-of-the-art system may be able to infer the relation using distributional information about the phrase the Sunshine State, but is likely to have limited evidence for the decision that it is coreferential with Florida rather than with Skynyrd. |
| Approach: | They propose to forward coreference input to relation extraction system and reward them for producing triples that are found in knowledge bases. |
| Outcome: | The proposed approach improves over the state-of-the-art by forwarding their input to a relation extraction system and rewarding resolvers for producing triples that are found in knowledge bases. |
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension (D19-61)
Copied to clipboard
| Challenge: | Existing knowledge bases are heavily biased towards English, but Wikipedias cover very different topics in different languages. |
| Approach: | They propose a multilingual dataset that frams relation extraction as a machine reading problem. |
| Outcome: | The proposed model can be used to transfer models cross-lingually and improves knowledge base completion across languages. |
Ellipsis Resolution as Question Answering: An Evaluation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing models for ellipsis resolution in English are expensive and cumbersome . ellipas are hard, open problems in NLP, and can cause errors in translation, question answering, and dialogue understanding. |
| Approach: | They propose an alternative approach to ellipsis resolution based on question answering architectures. |
| Outcome: | The proposed model outperforms the current state of the art for ellipsis resolution in English . it shows that annotations can be useful for a subset of the known ellipas . |
Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021.acl-long)
Copied to clipboard
| Challenge: | Currently, document summarization is challenging even for humans. |
| Approach: | They propose a focus attention mechanism which encourages decoders to generate tokens that are topically similar to the input document. |
| Outcome: | The proposed method outperforms top-k and nucleus sampling methods on the BBC extreme summarization task and is more accurate than focus attention-based models. |
Varta: A Large-Scale Headline-Generation Dataset for Indic Languages (2023.findings-acl)
Copied to clipboard
| Challenge: | Varta dataset includes more than 41 million pairs of headlines and articles in 14 different Indic languages (and English) |
| Approach: | They present a large-scale multilingual dataset for headline generation in Indic languages. |
| Outcome: | The Varta dataset includes more than 41 million pairs of headlines and articles in 14 different Indic languages (and English) the data can be used to train strong language models that outperform competitive baselines in both NLU and NLG benchmarks. |
Sanskrit Sandhi Splitting using seq2(seq)2 (D18-1)
Copied to clipboard
| Challenge: | Existing methods for word splitting in Sanskrit have low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. |
| Approach: | They propose a deep learning architecture called Double Decoder RNN which predicts the location of the splits with 95% accuracy and 79.5% accuracy. |
| Outcome: | The proposed model outperforms the state-of-the-art in the problem of Chinese word segmentation with 79.5% accuracy and the existing model's generalization capability. |
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension (P18-1)
Copied to clipboard
| Challenge: | DuoRC contains 186,089 unique question-answer pairs created from 7680 movie plots . |
| Approach: | They propose a novel dataset for Reading Comprehension that motivates new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
| Outcome: | The proposed dataset motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages (2021.findings-acl)
Copied to clipboard
| Challenge: | Model-agnostic meta-learning (MAML) is a strategy to learn resource-poor languages in a sample-efficient fashion. |
| Approach: | They propose a model-agnostic meta-learning strategy that minimizes the expected risk across languages with a uniform prior . they propose 'minimax' and 'neyman-pearson' models that constrain the risk in each language to a maximum threshold. |
| Outcome: | The proposed model reduces the maximum risk across languages while constraining the risk in each language to a maximum threshold. |