William L. Hamilton


2021

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End-to-End Training of Neural Retrievers for Open-Domain Question Answering
Devendra Sachan | Mostofa Patwary | Mohammad Shoeybi | Neel Kant | Wei Ping | William L. Hamilton | Bryan Catanzaro
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-training. We first propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans, followed by supervised finetuning using question-context pairs. This approach leads to absolute gains of 2+ points over the previous best result in the top-20 retrieval accuracy on Natural Questions and TriviaQA datasets. We next explore two approaches for end-to-end training of the reader and retriever components in OpenQA models, which differ in the manner the reader ingests the retrieved documents. Our experiments demonstrate the effectiveness of these approaches as we obtain state-of-the-art results. On the Natural Questions dataset, we obtain a top-20 retrieval accuracy of 84%, an improvement of 5 points over the recent DPR model. We also achieve good results on answer extraction, outperforming recent models like REALM and RAG by 3+ points.

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Do Syntax Trees Help Pre-trained Transformers Extract Information?
Devendra Sachan | Yuhao Zhang | Peng Qi | William L. Hamilton
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Much recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) remains unclear, especially given recent studies highlighting how these models implicitly encode syntax. In this work, we systematically study the utility of incorporating dependency trees into pre-trained transformers on three representative information extraction tasks: semantic role labeling (SRL), named entity recognition, and relation extraction. We propose and investigate two distinct strategies for incorporating dependency structure: a late fusion approach, which applies a graph neural network on the output of a transformer, and a joint fusion approach, which infuses syntax structure into the transformer attention layers. These strategies are representative of prior work, but we introduce additional model design elements that are necessary for obtaining improved performance. Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks. However, our analysis also reveals a critical shortcoming of these models: we find that their performance gains are highly contingent on the availability of human-annotated dependency parses, which raises important questions regarding the viability of syntax-augmented transformers in real-world applications.

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Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Dora Jambor | Komal Teru | Joelle Pineau | William L. Hamilton
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Real-world knowledge graphs are often characterized by low-frequency relations—a challenge that has prompted an increasing interest in few-shot link prediction methods. These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time. In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this few-shot setting. We find that a simple, zero-shot baseline — which ignores any relation-specific information — achieves surprisingly strong performance. Moreover, experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information and only allows for exploiting the coarse-grained positional information of entities. Together, our findings challenge the implicit assumptions and inductive biases of prior work and highlight new directions for research in this area.

2020

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Learning an Unreferenced Metric for Online Dialogue Evaluation
Koustuv Sinha | Prasanna Parthasarathi | Jasmine Wang | Ryan Lowe | William L. Hamilton | Joelle Pineau
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to develop automatic dialogue evaluation metrics, but most of them do not generalize to unseen datasets and/or need a human-generated reference response during inference, making it infeasible for online evaluation. Here, we propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances, and leverages the temporal transitions that exist between them. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.

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Exploring the Limits of Simple Learners in Knowledge Distillation for Document Classification with DocBERT
Ashutosh Adhikari | Achyudh Ram | Raphael Tang | William L. Hamilton | Jimmy Lin
Proceedings of the 5th Workshop on Representation Learning for NLP

Fine-tuned variants of BERT are able to achieve state-of-the-art accuracy on many natural language processing tasks, although at significant computational costs. In this paper, we verify BERT’s effectiveness for document classification and investigate the extent to which BERT-level effectiveness can be obtained by different baselines, combined with knowledge distillation—a popular model compression method. The results show that BERT-level effectiveness can be achieved by a single-layer LSTM with at least 40× fewer FLOPS and only ∼3% parameters. More importantly, this study analyzes the limits of knowledge distillation as we distill BERT’s knowledge all the way down to linear models—a relevant baseline for the task. We report substantial improvement in effectiveness for even the simplest models, as they capture the knowledge learnt by BERT.

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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Jiapeng Wu | Meng Cao | Jackie Chi Kit Cheung | William L. Hamilton
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

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Structure Aware Negative Sampling in Knowledge Graphs
Kian Ahrabian | Aarash Feizi | Yasmin Salehi | William L. Hamilton | Avishek Joey Bose
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns. A crucial aspect of contrastive learning approaches is the choice of corruption distribution that generates hard negative samples, which force the embedding model to learn discriminative representations and find critical characteristics of observed data. While earlier methods either employ too simple corruption distributions, i.e. uniform, yielding easy uninformative negatives or sophisticated adversarial distributions with challenging optimization schemes, they do not explicitly incorporate known graph structure resulting in suboptimal negatives. In this paper, we propose Structure Aware Negative Sampling (SANS), an inexpensive negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node’s k-hop neighborhood. Empirically, we demonstrate that SANS finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization.

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Distilling Structured Knowledge for Text-Based Relational Reasoning
Jin Dong | Marc-Antoine Rondeau | William L. Hamilton
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

There is an increasing interest in developing text-based relational reasoning systems, which are capable of systematically reasoning about the relationships between entities mentioned in a text. However, there remains a substantial performance gap between NLP models for relational reasoning and models based on graph neural networks (GNNs), which have access to an underlying symbolic representation of the text. In this work, we investigate how the structured knowledge of a GNN can be distilled into various NLP models in order to improve their performance. We first pre-train a GNN on a reasoning task using structured inputs and then incorporate its knowledge into an NLP model (e.g., an LSTM) via knowledge distillation. To overcome the difficulty of cross-modal knowledge transfer, we also employ a contrastive learning based module to align the latent representations of NLP models and the GNN. We test our approach with two state-of-the-art NLP models on 13 different inductive reasoning datasets from the CLUTRR benchmark and obtain significant improvements.

2019

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CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
Koustuv Sinha | Shagun Sodhani | Jin Dong | Joelle Pineau | William L. Hamilton
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systematicity of NLU systems. Motivated by the classic work on inductive logic programming, CLUTRR requires that an NLU system infer kinship relations between characters in short stories. Successful performance on this task requires both extracting relationships between entities, as well as inferring the logical rules governing these relationships. CLUTRR allows us to precisely measure a model’s ability for systematic generalization by evaluating on held-out combinations of logical rules, and allows us to evaluate a model’s robustness by adding curated noise facts. Our empirical results highlight a substantial performance gap between state-of-the-art NLU models (e.g., BERT and MAC) and a graph neural network model that works directly with symbolic inputs—with the graph-based model exhibiting both stronger generalization and greater robustness.

2016

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Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing
Vinodkumar Prabhakaran | William L. Hamilton | Dan McFarland | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
William L. Hamilton | Kevin Clark | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning Linguistic Descriptors of User Roles in Online Communities
Alex Wang | William L. Hamilton | Jure Leskovec
Proceedings of the First Workshop on NLP and Computational Social Science