Papers with predicting
Can Transformer Language Models Predict Psychometric Properties? (2021.starsem-1)
Copied to clipboard
| Challenge: | Transformer-based language models (LMs) are gaining popularity on many NLP benchmark tasks. |
| Approach: | They use human responses to calculate psychometric properties of test items . they find transformer-based LMs predict psychometric property consistently well . |
| Outcome: | The transformer-based language models are able to predict psychometric properties of test items . the models can predict psychometries well in certain categories but poorly in others . |
Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation (N19-1)
Copied to clipboard
| Challenge: | Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus indicate the promise of our proposed method. |
| Approach: | They propose a task of predicting which terminology simultaneous interpreters will leave untranslated using supervised sequence taggers. |
| Outcome: | The proposed method predicts which terminology interpreters leave untranslated . it is based on an annotated version of the NAIST Simultaneous Translation Corpus . |
Attentive Interaction Model: Modeling Changes in View in Argumentation (N18-1)
Copied to clipboard
| Challenge: | Prior work on argumentation in the NLP community has focused mainly on the first goal and has missed more nuanced and complex details of viewpoints. |
| Approach: | They propose a neural architecture that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument to predict if the argument succeeded in altering the OH' s view. |
| Outcome: | The proposed model outperforms several baselines on discussions on the Change My View forum on Reddit. |
Decision-Focused Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing summarization methods define relevance based on textual information alone without incorporating insights about a particular decision. |
| Approach: | They propose a method that summarizes relevant information for a decision using full text . they then build a model that makes the decision based on the full text while accounting for textual non-redundancy. |
| Outcome: | The proposed method outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness. |
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. |
| Approach: | They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes . |
| Outcome: | The proposed model outperforms previous methods in terms of MAP and F1 scores. |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
Copied to clipboard
| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
Zero-Shot On-the-Fly Event Schema Induction (2023.findings-eacl)
Copied to clipboard
| Challenge: | a new approach to event processing uses large language models to generate source documents that can be curated without manual data collection. |
| Approach: | They propose a framework that generates a graphical representation of events in documents . they show that the model is more complete than previous supervised methods . |
| Outcome: | The proposed model is more complete than human-curated schemas in most scenarios. |
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | a number of information extraction tasks require task-specific training. |
| Approach: | They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model . |
| Outcome: | The proposed framework outperforms the existing methods on open information extraction tasks. |
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (P18-1)
Copied to clipboard
Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain
| Challenge: | Prior work focused on characterizing and detecting content exhibiting antisocial online behavior. |
| Approach: | They propose a task of predicting from the very start of a conversation whether it will get out of hand. |
| Outcome: | The proposed framework can detect early warning signs of antisocial behavior in online conversations. |
Modularized Syntactic Neural Networks for Sentence Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for sentence classification use local information of sub-trees, but new models use global context . |
| Approach: | They propose a tree-parallel mini-batch strategy for efficient training and predicting sentences . they propose to use syntax category labels to model sub-trees . |
| Outcome: | The proposed model outperforms state-of-the-art tree-based methods on the sentence classification task. |
Learning the Legibility of Visual Text Perturbations (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing adversarial attacks in NLP perturb text to produce visually similar strings ('ergo', 'rgo') which are legible to humans but degrade model performance. |
| Approach: | They use a human-annotated dataset comprising the legibility of visually perturbed text to build models that predict the legible inputs and rank them based on their legibility. |
| Outcome: | The proposed models achieve an F score of 0.91 and an accuracy of 0.86 in predicting which of two perturbations is more legible. |
Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task (2020.emnlp-main)
Copied to clipboard
| 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. |
Improved Neural Protoform Reconstruction via Reflex Prediction (2024.lrec-main)
Copied to clipboard
| Challenge: | comparative method allows linguists to infer protoforms from their reflexes based on sound change . authors argue that this approach ignores one of the most important aspects of the comparative approach . |
| Approach: | They propose a comparative method that allows linguists to infer protoforms from their reflexes . they propose to use a system where candidate protoform from a reconstruction model are reranked by a reflex prediction model. |
| Outcome: | The comparative method surpasses state-of-the-art methods on Chinese and Romance datasets. |
LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models generate tokens by updating high-dimensional representations and decoding from them at each timestep. |
| Approach: | They propose a framework that allows reasoning correction and length control based on derived ideal trajectories. |
| Outcome: | The proposed model can predict correctness and length control based on ideal trajectories. |
Wait! There’s a Way Out: A Decision Mechanism for Forecasting Conversational Derailment (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches make decision to "trigger" based on the estimated likelihood of derailment given the preceding utterances, implicitly assuming that the conversation’s future trajectory is fixed. |
| Approach: | They propose a method for decoupling the decision to trigger from derailment likelihood estimation. |
| Outcome: | The proposed method is inspired by the first human baseline on this task, which shows that humans achieve dramatically lower false positive rates by selectively deferring their decision to trigger when they anticipate that tension is likely to subside. |