Papers by Aditi Chaudhary
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection (2021.emnlp-main)
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| Challenge: | Using manual content to learn languages is expensive and time consuming. |
| Approach: | They propose a method for automatically identifying fine-grained lexical distinctions and extracting rules explaining them in a human- and machine-readable format. |
| Outcome: | The proposed method is able to identify fine-grained distinctions and explain them in a human- and machine-readable format. |
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations (D18-1)
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| Challenge: | Existing approaches to generalization to resource-rich languages are difficult . a recent study shows that word representations can be useful in low resource languages . |
| Approach: | They propose two approaches for improving generalization to low-resource languages by adapting continuous word representations using linguistically motivated subword units. |
| Outcome: | The proposed method improves generalization to low resource languages . it requires neither parallel corpora nor bilingual dictionaries and requires no parallel training . |
Salient Span Masking for Temporal Understanding (2023.eacl-main)
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| Challenge: | Salient Span Masking (SSM) has shown to be effective for closed-book question answering . authors of this study found that SSM alone improves performance on temporal tasks . |
| Approach: | They introduce Temporal Span Masking (TSM) to improve performance on temporal tasks . they find that SSM alone improves the downstream performance by +5.8 points . |
| Outcome: | The proposed approach improves performance on three temporal tasks by +5.8 points . the additional targeted spans achieved by adding the TSM task are the best . |
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting (2023.emnlp-main)
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| Challenge: | idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. |
| Approach: | They propose to use retrieval-augmented models to increase the accuracy of a strong pretrained machine translation model on idiomatic sentences by up to 13%. |
| Outcome: | The proposed techniques improve the accuracy of a strong pretrained model on idiomatic sentences by up to 13% in absolute accuracy, and holds potential benefits for non-idiomatic phrases. |
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
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| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |
Reducing Confusion in Active Learning for Part-Of-Speech Tagging (2021.tacl-1)
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| Challenge: | Existing algorithms for annotating parts of speech are not optimal for all languages. |
| Approach: | They propose to use a data selection algorithm to select useful training samples to minimize annotation cost. |
| Outcome: | The proposed strategy outperforms existing strategies on six typologically diverse languages. |
Automatic Extraction of Rules Governing Morphological Agreement (2020.emnlp-main)
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Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig
| Challenge: | Creating a descriptive grammar is an indispensable step for language documentation but it is tedious and time-consuming. |
| Approach: | They propose a framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. |
| Outcome: | The proposed framework extracts a grammatical specification that is nearly equivalent to those created with large amounts of gold-standard annotated data. |
It’s All Relative! – A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction (2024.findings-naacl)
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| Challenge: | Large language models generate synthetic query-document pairs by prompting with as few as 8 demonstrations. |
| Approach: | They propose to generate queries simultaneously for different labels by prompting with 8 demonstrations. |
| Outcome: | Extensive experimentation shows that synthetic queries generated in such a fashion improve performance. |
Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning (2023.findings-emnlp)
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| Challenge: | Language teachers need to be accessible and have the necessary resources to create effective content for their students. |
| Approach: | They propose to extract grammar descriptions from a natural text corpus that answer questions about morphosyntax and semantics from lexical corpus. |
| Outcome: | The proposed method is applied to two Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed resources for second language learning. |
Do Context-Aware Translation Models Pay the Right Attention? (2021.acl-long)
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| Challenge: | Context-aware machine translation models fail to leverage contextual information to resolve ambiguous words and pronouns. |
| Approach: | They propose a new dataset that includes supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. |
| Outcome: | The proposed model can automatically disambiguate pronouns and polysemous words when they are not in the same context. |
Evaluating the Morphosyntactic Well-formedness of Generated Texts (2021.emnlp-main)
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Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
| Challenge: | Text generation systems are ubiquitous in natural language processing applications, but evaluation of these systems remains a challenge, especially in multilingual settings. |
| Approach: | They propose a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphologically-rich rules of the language. |
| Outcome: | The proposed metric can evaluate the morphosyntactic well-formedness of text using its dependency parse and morphologically-rich rules of the language. |
DUBLIN: Visual Document Understanding By Language-Image Network (2023.emnlp-industry)
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Kriti Aggarwal, Aditi Khandelwal, Kumar Tanmay, Owais Khan Mohammed, Qiang Liu, Monojit Choudhury, Hardik Chauhan, Subhojit Som, Vishrav Chaudhary, Saurabh Tiwary
| Challenge: | DUBLIN is a pixel-based visual document understanding model that does not rely on OCR. |
| Approach: | They propose a pixel-based visual document understanding model that does not rely on OCR. |
| Outcome: | The proposed model performs on extractive tasks such as DocVQA, InfoVQA and AI2D, and strong performance on abstraction datasets such as VisualMRC and text captioning. |
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |