Papers by Asli Celikyilmaz

AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization (2021.eacl-main)

Challenge:	Current language models generate high-quality text, but are they copying it or have they learned generalizable linguistic abstractions?
Approach:	They propose a suite of analyses for assessing the novelty of generated text . they focus on sequential structure (n-grams) and syntactic structure (syntactical structure).
Outcome:	The proposed model-generated text is as novel as the baseline human-generated model- generated text, but it is copied substantially, the authors show .

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Keping Bi, Rahul Jha, Bruce Croft, Asli Celikyilmaz

Information Extraction & Text Mining

Challenge:	Existing studies on redundancy are focused on salience alone.
Approach:	They propose to combine salience and novelty to score redundancy in extractive summarization systems . they also propose to balance saliance and redundancies by scoring redundants first .
Outcome:	Empirical results show that AREDSUM-CTX scores salience first, then learns to balance saliency and redundancy.

Discourse Understanding and Factual Consistency in Abstractive Summarization (2021.eacl-main)

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Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi

Understanding In-Context Learning via Supportive Pretraining Data (2023.acl-long)

Challenge:	Existing abstractive summarization models often hallucinate information or generate factually incorrect summaries.
Approach:	They propose a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary.
Outcome:	The proposed framework generates abstracts with factual consistency and coherence significantly better than baselines.

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Xiaochuang Han, Daniel Simig, Todor Mihaylov, Yulia Tsvetkov, Asli Celikyilmaz, Tianlu Wang

Efficient Tool Use with Chain-of-Abstraction Reasoning (2025.coling-main)

Challenge:	In-context learning (ICL) is a form of learning that provides a handful of examples at inference time, but it is not well understood why it emerges as the model has never been specifically trained on such demonstrations.
Approach:	They adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL and compare it with random subsets of pretrain data.
Outcome:	The proposed method improves the model's ICL ability by 18% if it is continued on a small subset of pretraining data.

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Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang

Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)

Challenge:	Recent large language models have made progress at interpreting and executing instructions.
Approach:	They propose a method to decouple general reasoning from specialized knowledge . they propose to use abstract reasoning chains and domain tools to reify each chain .
Outcome:	The proposed method outperforms baseline methods on QA and mathematical reasoning domains.

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Marjan Ghazvininejad, Vladimir Karpukhin, Vera Gor, Asli Celikyilmaz

Responsible & Trustworthy NLP

Challenge:	Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research.
Approach:	They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function.
Outcome:	The proposed methods perform better on some generation tasks but don't generalize across all generation tasks.

Gender Biases in Automatic Evaluation Metrics for Image Captioning (2023.emnlp-main)

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Haoyi Qiu, Zi-Yi Dou, Tianlu Wang, Asli Celikyilmaz, Nanyun Peng

Multimodality

Challenge:	Pretrained evaluation metrics can perpetuate and amplify biases, causing inability to differentiate between biased and unbiased generations.
Approach:	They conduct a systematic study of gender biases in image captioning tasks . they show that pretrained models perpetuate and amplify biase .
Outcome:	The proposed model-based evaluation metrics have shown good correlations with human judgments in language generation tasks.

Chain-of-Verification Reduces Hallucination in Large Language Models (2024.findings-acl)

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Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston

Complementary Explanations for Effective In-Context Learning (2023.findings-acl)

Challenge:	Large language models can generate plausible but incorrect factual information, termed hallucinations, but they can still fail on lesser known facts.
Approach:	They develop a method that allows language models to deliberate on the responses they give in order to correct their errors.
Outcome:	The proposed method decreases hallucinations across a variety of tasks, including list-based questions, closed book MultiSpanQA and longform text generation.

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Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Veselin Stoyanov, Greg Durrett, Ramakanth Pasunuru

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) have remarkable capabilities in learning from expla- nations in prompts, but there has been limited understanding of exactly how these explana- tions function or why they are effective.
Approach:	They propose a maximal marginal relevance-based exemplar selection approach to construct exemplar sets that are both relevant and comple- mentary.
Outcome:	The proposed model improves in- context learning performance across three tasks on multiple LLMs.

Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)

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Elizabeth Clark, Asli Celikyilmaz, Noah A. Smith

Challenge:	Existing automatic metrics for evaluating text are expensive and time-consuming.
Approach:	They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings.
Outcome:	The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts.

Substance over Style: Document-Level Targeted Content Transfer (2020.emnlp-main)

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Allison Hegel, Sudha Rao, Asli Celikyilmaz, Bill Dolan

An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)

Challenge:	Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an entire document to fit a set of constraints.
Approach:	They propose a document-level targeted content transfer task that addresses the challenge of rewriting an entire document coherently by generating coherent and diverse rewrites that obey a constraint while remaining close to the original document.
Outcome:	The proposed model outperforms existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document.

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Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev

Information Retrieval

Challenge:	Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train.
Approach:	They propose three strategies to deal with the lengthy input problem and locate relevant information using long dialogue datasets.
Outcome:	The retrieve-then-summarize pipeline models yield the best performance on three long dialogue datasets.

A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks (2020.acl-main)

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Angela Lin, Sudha Rao, Asli Celikyilmaz, Elnaz Nouri, Chris Brockett, Debadeepta Dey, Bill Dolan

Multimodality

Challenge:	a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions.
Approach:	They propose an algorithm that learns pairwise alignments between different recipes . they then use a graph algorithm to derive a joint alignment between multiple video and text recipes based on the same recipe.
Outcome:	The proposed algorithm learns pairwise alignments between different recipes for the same dish.

Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks (2023.findings-emnlp)

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Zixuan Ke, Bing Liu, Wenhan Xiong, Asli Celikyilmaz, Haoran Li

The ART of LLM Refinement: Ask, Refine, and Trust (2024.naacl-long)

Challenge:	Existing methods for Continual Learning (CL) have limited KT and catastrophic forgetting . a new method overcomes CF by isolating the knowledge of each task .
Approach:	They propose a method to overcome catastrophic forgetting and encourage knowledge transfer . they propose to discover a sub-network for each task and a soft-masking mechanism to preserve the previous knowledge.
Outcome:	The proposed method outperforms baselines in classification, generation, information extraction and their mixture.

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Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ramakanth Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz

COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations and self-improve?
Approach:	They propose a reasoning with a refinement strategy called ART: Ask, Refine, and Trust that asks necessary questions to decide when an LLM should refine its output and uses it to affirm or deny trust.
Outcome:	The proposed reasoning with a refinement strategy achieves a performance gain of +5 points over baselines on two multistep reasoning tasks.

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Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi

Semantic Text Processing

Challenge:	Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge.
Approach:	They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language.
Outcome:	The proposed models can generate rich and diverse commonsense descriptions in natural language.

ALERT: Adapt Language Models to Reasoning Tasks (2023.acl-long)

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Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissi, Siddharth Verma, Zhijing Jin, Gargi Ghosh, Mona Diab, Asli Celikyilmaz

Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics (2022.findings-emnlp)

Challenge:	Large language models have shown increasing in-context learning capabilities with scaling up the model and data sizes.
Approach:	They propose a benchmark and suite of analyses to evaluate reasoning skills of large language models.
Outcome:	The proposed model compares pre-trained and fine-tuned models on tasks that require reasoning skills to solve.

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Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami

Robustness in NLP

Challenge:	Recent studies have revealed the vulnerability of dialogue state tracking models to distributional shifts, resulting in poor performance.
Approach:	They present a toolkit for standardized and comprehensive dialogue state tracking diagnoses that provides a richer summary of strengths and weaknesses.
Outcome:	The proposed toolkit shows that different classes of DST models have clear strengths and weaknesses, while generation models are more promising for handling language variety and span-based classification models are robust to unseen entities.

RMM: A Recursive Mental Model for Dialogue Navigation (2020.findings-emnlp)

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Homero Roman Roman, Yonatan Bisk, Jesse Thomason, Asli Celikyilmaz, Jianfeng Gao

Natural Language Interfaces

Challenge:	Existing work on language-guided robots focuses on the latter, but little attention is paid to the guiding agent.
Approach:	They propose a two-agent task where one agent navigates and asks questions that a second, guiding agent answers.
Outcome:	The proposed model can be generalized to novel environments.

ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization (2024.findings-acl)

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David Wan, Koustuv Sinha, Srini Iyer, Asli Celikyilmaz, Mohit Bansal, Ramakanth Pasunuru

Deep Learning for Dialogue Systems (C18-3)

Challenge:	Recent-proposed evaluation metrics for large language models have a preference-bias . however, such metrics often lack interpretability and only offer a single score .
Approach:	They propose a metric that leverages the power of large language models to perform two sub-tasks: decomposing summaries into atomic content units and validating them against the source document.
Outcome:	The proposed metric improves faithfulness scores on three summarization evaluation benchmarks by 3% compared to the next-best metric.

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Yun-Nung Chen, Asli Celikyilmaz, Dilek Hakkani-Tür

Natural Language Interfaces

Challenge:	Using deep learning to build robust and scalable spoken dialogue systems is still a challenging task.
Approach:	tutorial focuses on an overview of dialogue system development . goal-oriented spoken dialogue systems are most prominent component in virtual personal assistants .
Outcome:	This tutorial focuses on an overview of dialogue system development while summarizing the challenges.

Discourse-Aware Neural Rewards for Coherent Text Generation (N18-1)

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Antoine Bosselut, Asli Celikyilmaz, Xiaodong He, Jianfeng Gao, Po-Sen Huang, Yejin Choi

EmailSum: Abstractive Email Thread Summarization (2021.acl-long)

Challenge:	Existing approaches to train text generation models using cross-entropy loss do not always correlate well with achieving high scores on commonly used evaluation measures.
Approach:	They propose to use discourse-aware rewards to model cross-sentence ordering to approximate desired discourse structure to train a model of long, coherent text.
Outcome:	The proposed model produces more coherent and less repetitive text than models trained with cross-entropy or with commonly used scores as rewards.

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Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, Mohit Bansal

RESPROMPT: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models (2024.naacl-long)

Challenge:	Recent years have brought about interest in the task of summarizing conversation threads.
Approach:	They develop an email thread summarization dataset that contains human-annotated short and long email threads over a wide variety of topics.
Outcome:	The proposed dataset contains human-annotated short (30 words) and long (100 words) summaries of 2,549 email threads over a wide variety of topics.

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Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilmaz

Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries (2022.naacl-main)

Challenge:	Chain-of-thought (CoT) has impressively unlocked the reasoning potential of large language models (LLMs), but it falls short when tackling problems that require multiple reasoning steps.
Approach:	They propose a new prompting strategy that advances multi-step reasoning in LLMs by integrating necessary connections into prompts.
Outcome:	The proposed strategy improves multi-step reasoning accuracy and improves reasoning accuracy across math, sequential, and commonsense domains.

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Xiangru Tang, Alexander Fabbri, Haoran Li, Ziming Mao, Griffin Adams, Borui Wang, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev

Information Extraction & Text Mining

Challenge:	Existing pre-trained summarization models produce text that is factually inconsistent with the input.
Approach:	They present a scale-based scale for Likert rating and a scoring algorithm for Best-Worst Scaling to improve crowdsourcing reliability.
Outcome:	The proposed model is more reliable than existing models on two news summarization datasets.

Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization (2021.naacl-main)

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Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao

Semantic Text Processing

Challenge:	Abstractive summarization is the task of generating a concise summary of input documents . a middle-aged man and a young girl died after they were unable to avoid the plane .
Approach:	They propose a model that enriches the original Transformer with a Tensor Product Representation for abstractive summarization.
Outcome:	The proposed model outperforms the Transformer and the original TP-Transformer significantly on several datasets.

CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning (2022.naacl-main)

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Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization (2021.naacl-main)

Challenge:	Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization.
Approach:	They propose a typology of factual errors to better understand hallucinations generated by current models and a contrastive fine-tuning strategy to improve the factual consistency and overall quality of summaries.
Outcome:	The proposed model significantly reduces all kinds of factual errors on both SAMSum dialogue summarization and AMI meeting summarizing datasets.

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Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev

Branch-Solve-Merge Improves Large Language Model Evaluation and Generation (2024.naacl-long)

Challenge:	Existing work on meeting summarization tasks is limited to short summaries that cover all the content of a meeting.
Approach:	They propose a query-based multi-domain meeting summarization task that generates a single short summary of meetings based on a transcript.
Outcome:	The proposed task is based on 1,808 query-summary pairs over 232 meetings in multiple domains.

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Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, Xian Li

Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models (2023.eacl-main)

Challenge:	Large Language Models (LLMs) are often used for multi-faceted language generation and evaluation tasks that require complex user constraints or taking into account multiple aspects and criteria.
Approach:	They propose a Large Language Model program that consists of branch, solve, and merge modules that are parameterized with specific prompts to the base LLM.
Outcome:	The proposed program improves the evaluation correctness and consistency for each LLM by up to 26%, reducing length and pairwise position biases by up 50%, and allowing LLaMA-2-chat to match or outperform GPT-4 on most domains.

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Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer

PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking (2020.emnlp-main)

Challenge:	Pretrained language models store a large amount of factual information that can be elicited by prompting or finetuning.
Approach:	They propose methods to measure model factual beliefs and update incorrect beliefs in models . they propose a new visualization tool that shows relationships between stored model beliefs .
Outcome:	The proposed methods improve models' consistency and accuracy, the authors show . their methods outperform existing methods in more difficult settings, the paper shows .

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Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, Jianfeng Gao

Crystal: Introspective Reasoners Reinforced with Self-Feedback (2023.emnlp-main)

Challenge:	a novel task is to generate a coherent narrative consistent with an outline . large-scale language models are not sufficient in generating coherent narratives for the given outline despite their impressive generation performance .
Approach:	They propose a task of outline-conditioned story generation that generates a coherent narrative . they propose 'plotmachines' that tracks dynamic plot states and learns different writing styles .
Outcome:	The proposed model can generate a coherent story by tracking the dynamic plot states while conditioning on the input outline while generating the full story.

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Jiacheng Liu, Ramakanth Pasunuru, Hannaneh Hajishirzi, Yejin Choi, Asli Celikyilmaz

GO FIGURE: A Meta Evaluation of Factuality in Summarization (2021.findings-acl)

Challenge:	Existing knowledge-augmented reasoning methods fail to capture the introspective nature of knowledge required in commonsense reasoning.
Approach:	They propose a method to develop an introspective commonsense reasoner that introspects for knowledge statements related to the given question and makes an informed prediction.
Outcome:	The proposed method outperforms standard supervised finetuning and chain-of-thought distilled methods and enhances the transparency of the commonsense reasoning process.

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Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, Jianfeng Gao

The Amazing World of Neural Language Generation (2020.emnlp-tutorials)

Challenge:	Recent advances in text generation systems produce fluent, coherent, relevant, and factually correct text.
Approach:	They propose a metaevaluation framework for evaluating factuality evaluation metrics . they propose five necessary conditions to evaluate factual metrics on diagnostic factuity data .
Outcome:	The proposed framework provides robust evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics.

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Yangfeng Ji, Antoine Bosselut, Thomas Wolf, Asli Celikyilmaz

reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs (2025.emnlp-main)

Challenge:	Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning.
Approach:	They will discuss how and why NLG models succeed/fail at generating coherent text.
Outcome:	This paper will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications.

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Zhaofeng Wu, Michihiro Yasunaga, Andrew Cohen, Yoon Kim, Asli Celikyilmaz, Marjan Ghazvininejad

Robustness in NLP

Challenge:	Existing reward models have a high performance on benchmarks, but performance degradation is often due to overfitting.
Approach:	They propose to explicitly train reward models to assign similar scores to paraphrases to improve their robustness.
Outcome:	The proposed model reduces degradation by half for the Chat Hard subset in RewardBench.

MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation (2023.findings-acl)

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Swarnadeep Saha, Xinyan Yu, Mohit Bansal, Ramakanth Pasunuru, Asli Celikyilmaz

Deep Communicating Agents for Abstractive Summarization (N18-1)

Challenge:	MURMUR generates highly faithful and correct reasoning paths that lead to 26% more logically consistent summaries on LogicNLG compared to direct prompting.
Approach:	They propose a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning that generates reasoning paths using neural and symbolic modules with specific linguistic and logical skills.
Outcome:	The proposed method improves on two data-to-text generation tasks, while achieving comparable performance to fine-tuned GPT-2 on out-of-domain data.

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Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi

Learning Compressed Sentence Representations for On-Device Text Processing (P19-1)

Challenge:	Empirical results show that multiple communicating agents produce a better summary than extractive summarization.
Approach:	They propose an encoder-decoder architecture that uses deep communicating agents to represent a long document for abstractive summarization.
Outcome:	Empirical results show that multiple communicating agents produce a better summary than baselines.

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Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz, Lawrence Carin

Representation Learning

Challenge:	Existing methods for learning sentence embeddings assume they are continuous and real-valued.
Approach:	They propose four different strategies to transform continuous and generic sentence embeddings into a binarized form while preserving their rich semantic information.
Outcome:	The proposed methods reduce storage requirements by over 98% and improve performance on downstream tasks.

STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension (2022.emnlp-main)

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Borui Wang, Chengcheng Feng, Arjun Nair, Madelyn Mao, Jai Desai, Asli Celikyilmaz, Haoran Li, Yashar Mehdad, Dragomir Radev

Natural Language Interfaces

Challenge:	Abstractive dialogue summarization is an important standalone task in natural language processing, but no previous work has explored whether it can be used to boost an NLP system's performance on other important dialogue comprehension tasks.
Approach:	They propose a novel type of dialogue summarization task that decomposes and imitates the hierarchical, systematic and structured mental process that human beings usually go through when understanding and analyzing dialogues.
Outcome:	The proposed model improves the performance of transformer encoder language models on two important dialogue comprehension tasks.

Robust Navigation with Language Pretraining and Stochastic Sampling (D19-1)

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Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi

Robustness in NLP

Challenge:	Existing methods to learn visual representations and action decoding schemes are limited to previously unseen instructions and environments.
Approach:	They propose a stochastic sampling scheme to reduce the gap between the expert actions in training and sampled actions in test to correct its own mistakes.
Outcome:	The proposed methods achieve 6% absolute gain over the previous best results on the Room-to-Room benchmark.

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models (P19-1)

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Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin

ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection (2022.emnlp-main)

Challenge:	Variational autoencoders (VAEs) have received much attention as an end-to-end architecture for text generation with latent variables.
Approach:	They propose to leverage several multi-level structures to learn a variational autoencoder model for generating long, and coherent text.
Outcome:	The proposed model produces more coherent and less repetitive long text compared to baselines and mitigates posterior collapse issue.

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Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab

Responsible & Trustworthy NLP

Challenge:	Hate speech detection is complex and requires commonsense reasoning and social nuance . prior work has shown that even humans cannot achieve a high agreement on whether a post constitutes HS .
Approach:	They frame a few-shot learning task to decompose a hate speech detection task into its "constituent" parts. they show that infusing commonsense knowledge from reasoning datasets improves the performance even further.
Outcome:	The proposed method outperforms baseline methods in the 16-shot case.

Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing (N19-1)

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Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin