Papers by Manaal Faruqui
Can Sequence-to-Sequence Transformers Naturally Understand Sequential Instructions? (2023.starsem-1)
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| Challenge: | Using a limited annotation budget, we can greatly improve the performance on intermediate steps with a drop in final-step performance. |
| Approach: | They propose to use a pre-supervised sequence-to-sequence transformer to provide training signals on intermediate steps with zero gold supervision instead of only final-step supervision to improve performance. |
| Outcome: | The proposed model significantly improves on intermediate steps with a drop in final-step performance on one subtask, but also shows decreased performance on another subtask. |
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)
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| Challenge: | a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence. |
| Approach: | They use Wikipedia edit history to mine atomic edits across 8 languages . they find edits contain instances in which a human editor has inserted a single phrase into, or deleted a contiguous phrase from, an existing sentence. |
| Outcome: | The data show that edits differ from the language observed in standard corpora and that models trained on edits encode different aspects of semantics and discourse than models trained in raw text. |
Efficient Encoders for Streaming Sequence Tagging (2023.eacl-main)
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| Challenge: | Existing bidirectional encoders require a restart when a new token is received. |
| Approach: | They propose a Hybrid Encoder with Adaptive Restart that enables asynchronous encoding of a new token in an incremental streaming input. |
| Outcome: | The proposed encoder offers FLOP savings in streaming settings up to 71.1% and outperforms bidirectional encoders for streaming predictions by up to +0% streaming exact match. |
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (P19-1)
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| Challenge: | Existing text generation metrics rely on reference texts, such as BLEU and ROUGE, but they are too expensive to apply repeatedly. |
| Approach: | They propose a metric which aligns n-grams from the generated texts to the semi-structured data before computing their precision and recall. |
| Outcome: | The proposed metric correlates with human judgments better than existing text generation metrics while being easier to use. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
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| Challenge: | Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines. |
| Approach: | They propose a conditioned text generation model that uses a template-based approach to generate content from input text. |
| Outcome: | The proposed model outperforms baselines on abstractive text summarization and data-to-text generation. |
ToTTo: A Controlled Table-To-Text Generation Dataset (2020.emnlp-main)
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Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
| Approach: | They propose a controlled task where annotators directly revise existing Wikipedia sentences to generate a one-sentence description. |
| Outcome: | The proposed task produces a one-sentence description from a Wikipedia table and highlighted cells. |
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation (2025.naacl-long)
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Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities across various cognitive tasks. |
| Approach: | They propose a high-quality evaluation dataset to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. |
| Outcome: | The proposed framework improves performance in end-to-end RAG scenarios. |
UniMorph 2.0: Universal Morphology (L18-1)
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Christo Kirov, Ryan Cotterell, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sabrina J. Mielke, Arya McCarthy, Sandra Kübler, David Yarowsky, Jason Eisner, Mans Hulden
| Challenge: | The Universal Morphology project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. |
| Approach: | They propose to use a universal tagset to annotate morphological data using a schema that includes a lemma and a bundle of morphology features. |
| Outcome: | The project releases annotated morphological data using a universal tagset, the UniMorph schema. |
Learning To Split and Rephrase From Wikipedia Edit History (D18-1)
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| Challenge: | Performing split and rephrase tasks is one of the main operations in text simplification, alongside paraphrasing and dropping less salient content. |
| Approach: | They propose to use Wikipedia's edit history to extract a rich new dataset for the task. |
| Outcome: | The proposed model scores 32 BLEU points above the previous best on the WebSplit benchmark. |
TIMEDIAL: Temporal Commonsense Reasoning in Dialog (2021.acl-long)
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| Challenge: | Existing studies on pre-trained language models for dialog reasoning fail to understand context correctly. |
| Approach: | They propose to use a crowd-sourced English task and a time-based task to test models' temporal reasoning abilities in dialogs. |
| Outcome: | The proposed task and crowd-sourced English challenge set show that even the best performing models struggle on this task compared to humans. |
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering (2021.findings-acl)
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| Challenge: | Disfluencies are an under-studied topic in NLP, even though it is ubiquitous in human conversation. |
| Approach: | They propose a challenge question answering dataset where humans introduce contextual disfluencies in previously fluent questions. |
| Outcome: | The proposed dataset shows that existing models degrade significantly when tested on DISFL-QA in a zero-shot setting. |
Identifying Well-formed Natural Language Questions (D18-1)
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| Challenge: | Understanding search queries is a hard problem as it involves dealing with “word salad” text. |
| Approach: | They propose to identify a well-formed natural language question from a dataset of 25,100 questions. |
| Outcome: | The proposed classifier improves the performance of neural sequence-to-sequence models for generating questions for reading comprehension. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |
Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation (2024.emnlp-main)
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| Challenge: | evaluating large language models' output is difficult due to the high cost of human evaluation. |
| Approach: | They propose a family of foundational large autorater models that train on over 100 quality assessment tasks. |
| Outcome: | The proposed model outperforms models on 8 of 12 autorater benchmarks on 53 quality assessment tasks. |