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Found 59 papers, 25 papers with code
A Simple Unsupervised Approach for Coreference Resolution using Rule-based Weak Supervision
Labeled data for the task of Coreference Resolution is a scarce resource, requiring significant human effort.
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc.
Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation
Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog.
Opportunities and Challenges in Neural Dialog Tutoring
Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors.
Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference
Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference.
Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing
Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics.
Distilling Multi-Step Reasoning Capabilities of Large Language Models into Smaller Models via Semantic Decompositions
In this paper, we propose a knowledge distillation approach, that leverages the step-by-step CoT reasoning capabilities of larger models and distils these reasoning abilities into smaller models.
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
On both automatic and human quality evaluations, we find that LMs constrained with desirable question properties generate superior questions and improve the overall performance of a math word problem solver.
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.
Autoregressive Structured Prediction with Language Models
Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks.
Ranked #1 on
Relation Extraction
on CoNLL04
(RE+ Micro F1 metric)
Investigating the Role of Centering Theory in the Context of Neural Coreference Resolution Systems
Our analysis further shows that contextualized embeddings contain much of the coherence information, which helps explain why CT can only provide little gains to modern neural coreference resolvers which make use of pretrained representations.
A Bilingual Parallel Corpus with Discourse Annotations
The BWB corpus consists of Chinese novels translated by experts into English, and the annotated test set is designed to probe the ability of machine translation systems to model various discourse phenomena.
Differentially Private Language Models for Secure Data Sharing
To protect the privacy of individuals whose data is being shared, it is of high importance to develop methods allowing researchers and companies to release textual data while providing formal privacy guarantees to its originators.
Adapters for Enhanced Modeling of Multilingual Knowledge and Text
Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages.
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.
Longtonotes: OntoNotes with Longer Coreference Chains
Ontonotes has served as the most important benchmark for coreference resolution.
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MORALCOT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments.
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.
Probing via Prompting
We then examine the usefulness of a specific linguistic property for pre-training by removing the heads that are essential to that property and evaluating the resulting model's performance on language modeling.
A Structured Span Selector
Many natural language processing tasks, e. g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them.
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance
We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese.
Calibration of Machine Reading Systems at Scale
We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings.
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang
We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words.
Logical Fallacy Detection
In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).
What Has Been Enhanced in my Knowledge-Enhanced Language Model?
We conduct experiments to verify that our GCS can indeed be used to correctly interpret the KI process, and we use it to analyze two well-known knowledge-enhanced LMs: ERNIE and K-Adapter, and find that only a small amount of factual knowledge is integrated in them.
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming
These rules are learned in an online manner and applied with an ASP solver to predict an action for the agent.
Inductive logic programming
Natural Language Understanding
+2
Case-based Reasoning for Better Generalization in Textual Reinforcement Learning
Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency.
Out-of-Distribution Generalization
reinforcement-learning
+2
On Learning the Transformer Kernel
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers.
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.
"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.
Differentiable Subset Pruning of Transformer Heads
Multi-head attention, a collection of several attention mechanisms that independently attend to different parts of the input, is the key ingredient in the Transformer.
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL).
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks
In this paper, we present an approach to improve the robustness of BERT language models against word substitution-based adversarial attacks by leveraging adversarial perturbations for self-supervised contrastive learning.
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.
Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach
However, due to the inter-dependence of various phenomena and randomness of training probe models, detecting how these representations encode the rich information in these linguistic graphs remains a challenging problem.
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Standard automatic metrics, e. g. BLEU, are not reliable for document-level MT evaluation.
Deep Clustering of Text Representations for Supervision-free Probing of Syntax
We explore deep clustering of text representations for unsupervised model interpretation and induction of syntax.
Stronger Transformers for Neural Multi-Hop Question Generation
In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text.
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making.
Ranked #1 on
Commonsense Reasoning for RL
on commonsense-rl
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches
We introduce a number of RL agents that combine the sequential context with a dynamic graph representation of their beliefs of the world and commonsense knowledge from ConceptNet in different ways.
Knowledge Graph Embedding Compression
Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications.
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments.
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems
Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems.
Discourse in Multimedia: A Case Study in Information Extraction
At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.
Contextual Parameter Generation for Universal Neural Machine Translation
We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation.
Self-Training for Jointly Learning to Ask and Answer Questions
The two tasks of question answering and question generation are usually tackled separately in the NLP literature.
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition
We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems
These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.
Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks
As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks.
Learning Concept Taxonomies from Multi-modal Data
We study the problem of automatically building hypernym taxonomies from textual and visual data.
Science Question Answering using Instructional Materials
We provide a solution for elementary science test using instructional materials.
