Search Results for author:
Found 53 papers, 22 papers with code
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
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 research activity in different areas on different datasets is likely to become increasingly difficult.
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics
Tasks, Datasets and Evaluation Metrics are important concepts for understanding experimental scientific papers.
D2S: Document-to-Slide Generation Via Query-Based Text Summarization
Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming.
Ensembling Graph Predictions for AMR Parsing
In many machine learning tasks, models are trained to predict structure data such as graphs.
Ranked #2 on
AMR Parsing
on LDC2020T02
(using extra training data)
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on
Question Generation
on FairytaleQA
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization
Generating educational questions of fairytales or storybooks is vital for improving children's literacy ability.
Bridging Anaphora Resolution as Question Answering
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 mention information is given.
Fine-grained Information Status Classification Using Discourse Context-Aware BERT
Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS).
End-to-end Neural Information Status Classification
In this paper, we propose an end-to-end neural approach for information status classification.
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims.
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models
The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells.
On the Role of Summary Content Units in Text Summarization Evaluation
At the heart of the Pyramid evaluation method for text summarization lie human written summary content units (SCUs).
CiteBench: A benchmark for Scientific Citation Text Generation
To address this challenge, we propose CiteBench: a benchmark for citation text generation that unifies multiple diverse datasets and enables standardized evaluation of citation text generation models across task designs and domains.
'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism
Science journalism refers to the task of reporting technical findings of a scientific paper as a less technical news article to the general public audience.
Constrained Multi-Task Learning for Bridging Resolution
We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers.
Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization
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 difficult tasks, such as evaluating automatic summarization.
Employing Argumentation Knowledge Graphs for Neural Argument Generation
Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines.
Enhanced Word Representations for Bridging Anaphora Resolution
Most current models of word representations(e. g., GloVe) have successfully captured fine-grained semantics.
A Deterministic Algorithm for Bridging Anaphora Resolution
Additionally, we further improve the results for bridging anaphora resolution reported in Hou (2018) by combining our simple deterministic approach with Hou et al.(2013b)'s best system MLN II.
Unrestricted Bridging Resolution
The second stage, bridging antecedent selection, finds the antecedents for all predicted bridging anaphors.
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks.
Argumentation Quality Assessment: Theory vs. Practice
Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches.
Computational Argumentation Quality Assessment in Natural Language
Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation.
Know Who Your Friends Are: Understanding Social Connections from Unstructured Text
Having an understanding of interpersonal relationships is helpful in many contexts.
Argument Relation Classification Using a Joint Inference Model
In this paper, we address the problem of argument relation classification where argument units are from different texts.
Incremental Fine-grained Information Status Classification Using Attention-based LSTMs
Information status plays an important role in discourse processing.
Extracting Factual Min/Max Age Information from Clinical Trial Studies
Population age information is an essential characteristic of clinical trials.
Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention
Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS).
A Summarization System for Scientific Documents
We present a novel system providing summaries for Computer Science publications.
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports
Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Probing for Bridging Inference in Transformer Language Models
We probe pre-trained transformer language models for bridging inference.
End-to-End NLP Knowledge Graph Construction
This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers.
Overview of the 2021 Key Point Analysis Shared Task
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 2021.
Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead
Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication.
Finding Sub-task Structure with Natural Language Instruction
When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction.
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
NECE: Narrative Event Chain Extraction Toolkit
To understand a narrative, it is essential to comprehend the temporal event flows, especially those associated with main characters; however, this can be challenging with lengthy and unstructured narrative texts.
End-to-End Neural Bridging Resolution
The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers are weaker than those used for entity coreference resolution.
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales
Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literature analyzing such biases in children stories.
How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study
Our findings challenge the previously asserted general superiority of in-context learning (ICL) for OOD.
Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization
Pre-trained language models (LMs) perform well in In-Topic setups, where training and testing data come from the same topics.
