AUTOMATIC AND HUMAN EVALUATION

Data-to-text Generation with Macro Planning (2021.tacl-1)

Challenge:	DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains .
Approach:	They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains .
Outcome:	The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems.

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Ratish Puduppully, Mirella Lapata

Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings (2026.tacl-1)

Challenge:	Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently .
Approach:	They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets .
Outcome:	The proposed model outperforms baselines on two data-to-text benchmarks . it uses the encoderdecoder architecture and is compared with existing models .

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Miguel Moura Ramos, Tomás Almeida, Daniel Vareta, Filipe Azevedo, Sweta Agrawal, Patrick Fernandes, André F. T. Martins

Machine Translation

Challenge:	Reinforcement learning (RL) is an effective and robust method for training neural machine translation systems.
Approach:	They propose a method that leverages fine-grained, token-level quality assessments . they use a state-of-the-art quality estimation system as their token- level reward model .
Outcome:	The proposed approach leverages fine-grained, token-level quality assessments along with error severity levels to improve translation quality.

Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model (2022.coling-1)

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Sixing Wu, Ying Li, Ping Xue, Dawei Zhang, Zhonghai Wu

TwistList: Resources and Baselines for Tongue Twister Generation (2023.acl-short)

Challenge:	Pre-trained language models can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding.
Approach:	They propose a two-stage framework to integrate a linearized knowledge into plan text using a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact.
Outcome:	The proposed framework improves the performance of pre-trained language models by using section-aware strategies to encode the linearized knowledge.

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Tyler Loakman, Chen Tang, Chenghua Lin

Syntactic Text Processing

Challenge:	Previous work in phonetically-grounded language generation has focused on domains such as lyrics and poetry.
Approach:	They propose to use TwistList to generate phonetically constrained tongue twisters, a large annotated dataset consisting of 2.1K+ human-authored examples.
Outcome:	The proposed models perform better than pre-trained models with limited training and data and no explicit phonetic knowledge.

Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)

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Young-Jun Lee, Chae-Gyun Lim, Ho-Jin Choi

EmpHi: Generating Empathetic Responses with Human-like Intents (2022.naacl-main)

Challenge:	Empathy is a multi-dimensional concept consisting of cognitive and affective aspects.
Approach:	They propose two new in-context example selection methods that utilize emotion and situational information.
Outcome:	The proposed method is effective in measuring the degree of human empathy.

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Mao Yan Chen, Siheng Li, Yujiu Yang

Challenge:	Existing empathetic dialogue models lack emotion-dependent response generation . elaine mccartney: "i'm sorry to hear that! "
Approach:	They propose a model to generate empathetic responses with human-consistent intents . they aim to address the bias of the empathic intent distribution between epd models and humans .
Outcome:	The proposed model outperforms state-of-the-art models in terms of empathy, relevance, and diversity on automatic and human evaluation.

Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)

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Vishwajeet Kumar, Manish Joshi, Ganesh Ramakrishnan, Yuan-Fang Li

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)

Challenge:	Current neural network-based questions generation techniques take only one or two sentences as input.
Approach:	They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary .
Outcome:	The proposed model outperforms state-of-the-art systems in question generation from paragraphs in automatic and human evaluation.

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Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

Natural Language Interfaces

Challenge:	Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity.
Approach:	They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process.
Outcome:	The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation.

Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)

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Jiali Chen, Xusen Hei, Yuqi Xue, Zihan Wu, Jiayuan Xie, Yi Cai

Translating Web Search Queries into Natural Language Questions (L18-1)

Challenge:	Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA.
Approach:	They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning.
Outcome:	The proposed method significantly improves performance in automatic and human evaluation.

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Adarsh Kumar, Sandipan Dandapat, Sushil Chordia

Machine Translation

Challenge:	a new method to generate natural language questions from keyword-based queries is proposed . a synergy between query-to-question problem and standard machine translation (MT) model is found .
Approach:	They propose a method to generate well-formed natural language questions from keyword-based queries.
Outcome:	The proposed method is well-formed natural language question generated from keyword-based query.

Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation (2022.findings-naacl)

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Yujie Xing, Jinglun Cai, Nils Barlaug, Peng Liu, Jon Atle Gulla

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes (2021.emnlp-main)

Challenge:	Existing studies on open-domain conversational systems are limited to single corpus training and evaluation.
Approach:	They propose a method which encodes each corpus through a unique corpus embedding and a new word-level importance weighting method that integrates DF to the loss function.
Outcome:	The proposed methods gain significant improvements on both automatic and human evaluation.

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Hyunwoo Kim, Byeongchang Kim, Gunhee Kim

Expressing Visual Relationships via Language (P19-1)

Challenge:	Empathy is a complex cognitive ability based on the reasoning of others’ affective states.
Approach:	They propose a method to infer emotion cause words from utterances without a word-level label and a novel method to make dialogue models focus on targeted words in the input during generation.
Outcome:	The proposed method improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of automatic and human evaluation.

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Hao Tan, Franck Dernoncourt, Zhe Lin, Trung Bui, Mohit Bansal

Multimodality

Challenge:	Current studies on image captioning focus on single image, but there are no effective models for generating relational captions for two images.
Approach:	They propose a language-guided image editing dataset that contains real image pairs with corresponding editing instructions.
Outcome:	The proposed model outperforms baseline and existing methods on two datasets.

TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process (2020.findings-emnlp)

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Junsheng Kong, Zhicheng Zhong, Yi Cai, Xin Wu, Da Ren

Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (P19-1)

Challenge:	Empirical results show that generative models often use a single decoder to generate a complete response at a stroke.
Approach:	They propose a content-aware model with two-stage decoding process to separate content words from function words.
Outcome:	The proposed model outperforms competing models in automatic and human evaluation on two datasets.

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Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, Xu Sun

Reasoning

Challenge:	Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics.
Approach:	They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency.
Outcome:	The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.

Data-to-text Generation with Entity Modeling (P19-1)

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Ratish Puduppully, Li Dong, Mirella Lapata

Low-Resource Response Generation with Template Prior (D19-1)

Challenge:	Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to end.
Approach:	They propose an entity-centric neural architecture for data-to-text generation which uses hierarchical attention to create entity-specific representations which are dynamically updated.
Outcome:	The proposed model outperforms baselines in automatic and human evaluation on the RotoWire benchmark and a five-times larger dataset on the baseball domain.

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Ze Yang, Wei Wu, Jian Yang, Can Xu, Zhoujun Li

Multimodal Dialogue Response Generation (2022.acl-long)

Challenge:	Existing open domain response generation models are limited to paired data, but are less explored in real-world applications.
Approach:	They propose to train a neural response generation model with unpaired data and paired data as prior.
Outcome:	The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available.

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Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities (2020.coling-main)

Challenge:	Existing studies focus on multimodal dialogue models but neglect generation methods.
Approach:	They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain.
Outcome:	Experiments show that the proposed model can generate informative text and high-resolution image responses.

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Rossana Cunha, Thiago Castro Ferreira, Adriana Pagano, Fabio Alves

Challenges in Detoxifying Language Models (2021.findings-emnlp)

Challenge:	Data-to-text Natural Language Generation (NLG) is a computational process of generating natural language from non-linguistic data.
Approach:	They propose two extensions to a state-of-the-art encoder-decoder REG model that generates referring expressions to unseen entities.
Outcome:	The proposed model generates more meaningful referring expressions to unseen entities than the original system and related work.

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Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment (2023.emnlp-main)

Challenge:	Prior work often relies on automatic evaluation of LM toxicity.
Approach:	They evaluate toxicity mitigation strategies for automated and human evaluations . they find human raters disagree with high automatic toxicity scores after strong toxicity reduction interventions .
Outcome:	The proposed methods reduce LM toxicity but lower coverage for marginalized texts . human raters disagree with high toxicity scores after strong toxicity reduction interventions .

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Sky CH-Wang, Arkadiy Saakyan, Oliver Li, Zhou Yu, Smaranda Muresan

Reasoning

Challenge:	Current research on developing computational models of social norms has focused on American society.
Approach:	They propose to leverage a Chinese Q&A platform and a socialchiemistry dataset as proxies for contrasting cultural axes and align social situations cross-culturally.
Outcome:	The proposed model can reason across cultures using a Chinese Q&A platform and the existing socialChemistry dataset.

Mitigating Contradictions in Dialogue Based on Contrastive Learning (2022.findings-acl)

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Weizhao Li, Junsheng Kong, Ben Liao, Yi Cai

Multilingual Summarization with Factual Consistency Evaluation (2023.findings-acl)

Challenge:	Current chatbots generate fluent, informative responses but sometimes generate contradictory responses when interacting with human.
Approach:	They propose to use contrastive learning technique to mitigate contradiction issues in chatbots by minimizing the similarity between the target response and contradiction related negative example.
Outcome:	The proposed method outperforms existing methods on automatic and human evaluation while preserving response fluency.

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Roee Aharoni, Shashi Narayan, Joshua Maynez, Jonathan Herzig, Elizabeth Clark, Mirella Lapata

StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements (2024.emnlp-main)

Challenge:	Abstractive summarization models generate factually inconsistent summaries, reducing their utility for real-world applications.
Approach:	They propose to use data filtering and controlled generation to detect hallucinations in machine generated summaries.
Outcome:	The proposed models detect factual inconsistencies in machine generated summaries, but they focus on English only.

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Jillian Fisher, Skyler Hallinan, Ximing Lu, Mitchell Gordon, Zaid Harchaoui, Yejin Choi

Responsible & Trustworthy NLP

Challenge:	Authorship obfuscation methods that ignore author-specific stylistic features are often too rigid and lead to degradation of fluency and grammaticality.
Approach:	They propose an adaptive obfuscation method that perturbs stylistic elements of text . authors release a large set of 30K high-quality, long-form texts from a diverse set of 14 authors .
Outcome:	The proposed method outperforms state-of-the-art methods on an array of domains on automatic and human evaluation.

Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations (2024.findings-naacl)

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Dayeon Ki, Marine Carpuat

IgSEG: Image-guided Story Ending Generation (2021.findings-acl)

Challenge:	supervised systems have not replaced dedicated supervised models for machine translation tasks.
Approach:	They propose to guide LLMs to post-edit MT with feedback from MQM annotations . they then fine-tune the LLM to improve its ability to exploit the feedback .
Outcome:	The proposed model improves TER, BLEU and COMET scores on Chinese-English, English-German and English-Russian data.

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Qingbao Huang, Chuan Huang, Linzhang Mo, Jielong Wei, Yi Cai, Ho-fung Leung, Qing Li

Multimodality

Challenge:	Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories.
Approach:	They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges.
Outcome:	The proposed model outperforms baselines on automatic and human evaluation.

RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023.findings-acl)

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Ziwei Ji, Zihan Liu, Nayeon Lee, Tiezheng Yu, Bryan Wilie, Min Zeng, Pascale Fung

Structured Data in NLP

Challenge:	Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems.
Approach:	They propose a method to generate hallucinated responses using knowledge graphs . they propose local knowledge grounding to combine textual embeddings with corresponding KG embeddments . a global knowledge ground technique is also proposed to equip RHO with multi-hop reasoning abilities .
Outcome:	The proposed approach outperforms state-of-the-art methods on automatic and human evaluation by a large margin.

Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)

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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty

A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph (2020.findings-emnlp)

Challenge:	Inferring key insights from charts can be challenging and time-consuming.
Approach:	They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language.
Outcome:	The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores .

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Lin Qiao, Jianhao Yan, Fandong Meng, Zhendong Yang, Jie Zhou

Towards Abstractive Grounded Summarization of Podcast Transcripts (2022.acl-long)

Challenge:	Topic-to-essay generation is a promising task for natural language generation.
Approach:	They propose a Sentiment Controllable topic-to- essay generator with a Topic Knowledge Graph enhanced decoder to generate essays with only several given topic words.
Outcome:	The proposed model outperforms the state-of-the-art model on automatic and human evaluation.

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Kaiqiang Song, Chen Li, Xiaoyang Wang, Dong Yu, Fei Liu

Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)

Challenge:	Podcast summarization is of practical benefit to content providers and consumers . however, podcast summarizing faces significant challenges including factual inconsistencies . speech recognizers induce transcription errors and abstractive summarisation models may hallucinate .
Approach:	They propose a method to generate podcast summaries while grounding segments in specific regions of the transcript to allow full inspection of summary details.
Outcome:	The proposed method can produce an abstractive summary while grounding segments in specific regions of the transcript to allow full inspection of summary details.

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Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, Ting Liu

Pun-GAN: Generative Adversarial Network for Pun Generation (D19-1)

Challenge:	Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base.
Approach:	They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models.
Outcome:	The proposed framework outperforms baseline models on automatic and human evaluation on a Stanford Multi-turn Multi-domain task-oriented dialogue dataset.

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Fuli Luo, Shunyao Li, Pengcheng Yang, Lei Li, Baobao Chang, Zhifang Sui, Xu Sun

Robustness in NLP

Challenge:	Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses)
Approach:	They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses.
Outcome:	The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation.

PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning (2023.findings-emnlp)

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Chengfeng Dou, Zhi Jin, Wenpin Jiao, Haiyan Zhao, Yongqiang Zhao, Zhengwei Tao

Few-Shot Learning for Opinion Summarization (2020.emnlp-main)

Challenge:	In-context learning is a key task in health conversational assistants, but it is difficult to guarantee the specificity of the responses.
Approach:	They propose a plug-and-play medical dialogue system that provides a patient-centered medical interpretation service to users who are less knowledgeable about medical knowledge.
Outcome:	The proposed model improves the specificity of the patient-centered medical dialogues by providing them with real dialogues from similar patients as prompts.

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Arthur Bražinskas, Mirella Lapata, Ivan Titov

Responsible & Trustworthy NLP

Challenge:	a recent study shows that abstractive summarization models fail to capture their essential properties due to the high cost of summary production.
Approach:	They propose a few-shot framework for abstractive opinion summarization that bootstraps the output of an unsupervised model.
Outcome:	The proposed framework outperforms extractive and abstractive methods on Amazon and Yelp datasets.

On Learning Text Style Transfer with Direct Rewards (2021.naacl-main)

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Yixin Liu, Graham Neubig, John Wieting

Data-to-text Generation by Splicing Together Nearest Neighbors (2021.emnlp-main)

Challenge:	Existing methods for text style transfer lack parallel corpora, which makes it impossible to train supervised models.
Approach:	They propose to use semantic similarity metrics to explicitly assess the preservation of content between system outputs and inputs.
Outcome:	The proposed methods provide significant gains in automatic and human evaluation over strong baselines.

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Sam Wiseman, Arturs Backurs, Karl Stratos

Prompt-Based Editing for Text Style Transfer (2023.findings-emnlp)

Challenge:	Existing work on data-to-text generation relies on retrieved "neighbors" but instead generates text token-by-token, left-to right.
Approach:	They propose to splice together retrieved segments of text from "neighbor" source-target pairs to generate text token-by-token, left-to-right.
Outcome:	The proposed method performs on par with strong baselines in terms of automatic and human evaluation, but allows for more interpretable and controllable generation.

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Guoqing Luo, Yu Han, Lili Mou, Mauajama Firdaus

Low-Resource NLP

Challenge:	Text style transfer is a type of textual prompt that generates style-transferred texts word by word . early prediction errors may affect future word predictions.
Approach:	They propose a prompt-based editing approach to text style transfer using a pretrained language model.
Outcome:	The proposed approach outperforms existing systems with 20 times more parameters on three style-transfer benchmark datasets.

Envisioning Future from the Past: Hierarchical Duality Learning for Multi-Turn Dialogue Generation (2023.acl-long)

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Ang Lv, Jinpeng Li, Shufang Xie, Rui Yan

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)

Challenge:	Existing approaches to generate high quality responses rely on future text .
Approach:	They propose a hierarchical duality learning for dialogue to simulate human cognitive ability . they utilize hierarchically dualities at token hierarchy and utterance hierarchy to simulate duality .
Outcome:	The proposed model can generate high quality responses that connect both previous and follow-up dialogues.

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Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Structured Data in NLP

Challenge:	Existing methods for table-to-text generation suffer from poor faithfulness and low coverage.
Approach:	They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model.
Outcome:	The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness.

ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025.findings-emnlp)

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Shuzhong Lai, Chenxi Li, Junhong Lai, Yucun Zhong, Chenyu Yan, Xiang Li, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors (2024.emnlp-main)

Challenge:	Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, especially when directly employing LLMs as an intervention doctor.
Approach:	They propose a framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) they also propose 'role-play' strategy in which LLM act as autistic children to comprehensively evaluate the doctor model's capabilities at the dialogue level.
Outcome:	The proposed framework outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors.

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Nico Daheim, Jakub Macina, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan

Interpretable Question Answering on Knowledge Bases and Text (P19-1)

Challenge:	Existing models for dialog tutoring fail to detect student errors and tailor their feedback to them.
Approach:	They propose to build dialog tutoring models to scaffold students' problem-solving and verify student solutions by using automatic and human evaluation.
Outcome:	The proposed model improves the quality of the tutor response generation by detecting student errors and adjusting the feedback to the errors.

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Alona Sydorova, Nina Poerner, Benjamin Roth

Explainability & Interpretability in NLP

Challenge:	Existing evaluation paradigms for ML based question answering models are lacking . a lack of explanation methods has been proposed for QA models .
Approach:	They propose an automatic evaluation paradigm for explanation methods in ML based question answering models . they adapt post hoc explanation methods such as LIME and input perturbation to the model .
Outcome:	The proposed evaluation paradigm compares explanation methods with human annotations.

EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning (2024.lrec-main)

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Mingxiu Cai, Daling Wang, Shi Feng, Yifei Zhang

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

Challenge:	Existing studies lack the perception of fine-grained dialogue emotion propagation, and have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response.
Approach:	They propose to use commonsense reasoning and reinforcement learning to generate empathetic response based on in-context commonsensing and contextual reasoning to broaden cognitive boundaries.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluation.

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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang

Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task (2020.emnlp-main)

Challenge:	Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing.
Approach:	They propose a cross-lingual conversation summarization benchmark that explicitly considers source context.
Outcome:	The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations.

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Dongyeop Kang, Eduard Hovy

Detecting and Mitigating Hallucinations in Multilingual Summarisation (2023.emnlp-main)

Challenge:	despite the success of contextualized language models, language models cannot capture textual coherence of a long, multi-sentence document.
Approach:	They propose a paragraph completion task that predicts masked sentences in a sentence . they propose SSPlanner that predict what to say first and guides the pretrained model .
Outcome:	The proposed model outperforms baseline generation models on the paragraph completion task in automatic and human evaluation.

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Yifu Qiu, Yftah Ziser, Anna Korhonen, Edoardo Ponti, Shay Cohen

Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)

Challenge:	Existing faithfulness metrics for abstractive summarisation models focus on English . metric mFACT is best suited to detect hallucinations in cross-lingual transfer .
Approach:	They propose a method to evaluate the faithfulness of non-English summaries by translation-based transfer from multiple English faithfulness metrics.
Outcome:	The proposed method reduces hallucinations in cross-lingual transfer by weighing the loss of each training example by its faithfulness score.

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Tianyu Liu, Fuli Luo, Pengcheng Yang, Wei Wu, Baobao Chang, Zhifang Sui

Multimodality

Challenge:	Existing models for comprehensive descriptions for factual attribute-value tables might suffer from missing key attributes and groundless information problems.
Approach:	They propose a force attention method to encourage the generator to pay more attention to uncovered attributes to avoid potential key attributes missing.
Outcome:	The proposed model outperforms the state-of-the-art baselines on automatic and human evaluation.

INSPIRED: Toward Sociable Recommendation Dialog Systems (2020.emnlp-main)

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Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, Zhou Yu

Goal-Driven Explainable Clustering via Language Descriptions (2023.emnlp-main)

Challenge:	Existing studies on recommendation dialog systems lack a study on communication strategies used by human speakers for making successful and persuasive recommendations.
Approach:	They propose to annotate a dataset of human-human movie recommendation dialogs with sociable recommendation strategies.
Outcome:	The proposed model outperforms the baseline model in automatic and human evaluation.

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Zihan Wang, Jingbo Shang, Ruiqi Zhong

Explainability & Interpretability in NLP

Challenge:	Existing formulations neither consider the users’ goals nor explain clusters’ meanings.
Approach:	They propose a task formulation that represents both the goal and the explanations as free-form language descriptions.
Outcome:	The proposed method produces more accurate and goal-related explanations than previous methods.

Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)

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Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, Wei Xu

SciNews: From Scholarly Complexities to Public Narratives – a Dataset for Scientific News Report Generation (2024.lrec-main)

Challenge:	Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles.
Approach:	They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity.
Outcome:	The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1.

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Dongqi Pu, Yifan Wang, Jia E. Loy, Vera Demberg