52 citations · 109 across the 17 of their papers we have counts for
21 papers · 1 filter
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models
Myles Foley, Ambrish Rawat, Taesung Lee +3
The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption. While the pre-trained models can perform many tasks, such model…
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
Aniket Pramanick, Yufang Hou, Saif M. Mohammad +1
Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framewo…
Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization
Lining Zhang, Simon Mille, Yufang Hou +8
To prevent the costly and inefficient use of resources on low-quality annotations, we want a method for creating a pool of dependable annotators who can effectively complete diffic…
CiteBench: A benchmark for Scientific Citation Text Generation
Martin Funkquist, Ilia Kuznetsov, Yufang Hou +1
Science progresses by building upon the prior body of knowledge documented in scientific publications. The acceleration of research makes it hard to stay up-to-date with the recent…
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
Max Glockner, Yufang Hou, Iryna Gurevych
Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization
Zhenjie Zhao, Yufang Hou, Dakuo Wang +3
Generating educational questions of fairytales or storybooks is vital for improving children's literacy ability. However, it is challenging to generate questions that capture the i…
Overview of the 2021 Key Point Analysis Shared Task
Roni Friedman, Lena Dankin, Yufang Hou +3
We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 20…
Ensembling Graph Predictions for AMR Parsing
Hoang Thanh Lam, Gabriele Picco, Yufang Hou +5
In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into depe…
End-to-end Neural Information Status Classification
Yufang Hou
Most previous studies on information status (IS) classification and bridging anaphora recognition assume that the gold mention or syntactic tree information is given (Hou et al., 2…
D2S: Document-to-Slide Generation Via Query-Based Text Summarization
Edward Sun, Yufang Hou, Dakuo Wang +2
Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming…
Probing for Bridging Inference in Transformer Language Models
Onkar Pandit, Yufang Hou
We probe pre-trained transformer language models for bridging inference. We first investigate individual attention heads in BERT and observe that attention heads at higher layers p…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Bridging Anaphora Resolution as Question Answering
Yufang Hou
Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold men…
Corpus Wide Argument Mining -- a Working Solution
Liat Ein-Dor, Eyal Shnarch, Lena Dankin +10
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively sm…
A Summarization System for Scientific Documents
Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat +15
We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, explorat…
Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention
Yufang Hou
Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily d…
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
Yufang Hou, Charles Jochim, Martin Gleize +2
While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of researc…
Extracting Factual Min/Max Age Information from Clinical Trial Studies
Yufang Hou, Debasis Ganguly, Lea A. Deleris +1
Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples f…
A Deterministic Algorithm for Bridging Anaphora Resolution
Yufang Hou
Previous work on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b) use syntactic preposition patterns to calculate word relatedness. However, such patterns only…
Enhanced Word Representations for Bridging Anaphora Resolution
Yufang Hou
Most current models of word representations(e.g.,GloVe) have successfully captured fine-grained semantics. However, semantic similarity exhibited in these word embeddings is not su…