no code implementations • EMNLP 2020 • Shiva Upadhye, Leon Bergen, Andrew Kehler
Whereas there is a growing literature that probes neural language models to assess the degree to which they have latently acquired grammatical knowledge, little if any research has investigated their acquisition of discourse modeling ability.
1 code implementation • 25 Feb 2024 • Jianyou Wang, Kaicheng Wang, Xiaoyue Wang, Weili Cao, Ramamohan Paturi, Leon Bergen
This approach, representing a novel application of regularization techniques in synthetic data creation for IR, is tested on three recent IR tasks characterized by complex queries: DORIS-MAE, ArguAna, and WhatsThatBook.
1 code implementation • 21 Feb 2024 • Xiaoyue Wang, Jianyou Wang, Weili Cao, Kaicheng Wang, Ramamohan Paturi, Leon Bergen
We present the Benchmark of Information Retrieval (IR) tasks with Complex Objectives (BIRCO).
1 code implementation • 7 Oct 2023 • Jianyou Wang, Kaicheng Wang, Xiaoyue Wang, Prudhviraj Naidu, Leon Bergen, Ramamohan Paturi
In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical.
1 code implementation • NeurIPS 2021 • Leon Bergen, Timothy J. O'Donnell, Dzmitry Bahdanau
Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks.
no code implementations • 14 Apr 2021 • Leon Bergen, Dzmitry Bahdanau, Timothy J. O'Donnell
We present a model that jointly learns the denotations of words together with their groundings using a truth-conditional semantics.
1 code implementation • EMNLP 2020 • Charles Yu, Ryan Sie, Nico Tedeschi, Leon Bergen
Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages.
no code implementations • ACL 2020 • Eric Meinhardt, Eric Bakovic, Leon Bergen
Recent work has found evidence that natural languages are shaped by pressures for efficient communication {---} e. g. the more contextually predictable a word is, the fewer speech sounds or syllables it has (Piantadosi et al. 2011).
no code implementations • IJCNLP 2019 • Shraddha Barke, Rose Kunkel, Nadia Polikarpova, Eric Meinhardt, Eric Bakovic, Leon Bergen
Phonological processes are context-dependent sound changes in natural languages.
1 code implementation • 31 Oct 2017 • Eva Portelance, Amelia Bruno, Daniel Harasim, Leon Bergen, Timothy J. O'Donnell
The following technical report presents a formal approach to probabilistic minimalist grammar parameter estimation.