Publications (28)
A Causality-Guided Prediction of the TED Talk Ratings from the Speech-Transcripts using Neural Networks
Md Iftekhar Tanveer, Md Kamrul Hasan, Daniel Gildea +1
Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills. We use the largest open repository---TED Talks---to predict the ratin…
Feature-based Decipherment for Large Vocabulary Machine Translation
Iftekhar Naim, Daniel Gildea
Orthographic similarities across languages provide a strong signal for probabilistic decipherment, especially for closely related language pairs. The existing decipherment models,…
Leveraging Dependency Forest for Neural Medical Relation Extraction
Linfeng Song, Yue Zhang, Daniel Gildea +3
Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source…
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Linfeng Song, Zhiguo Wang, Mo Yu +3
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Prev…
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang +1
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence mode…
Strictly Breadth-First AMR Parsing
Chen Yu, Daniel Gildea
AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. We focus on the breadth-first strategy of this task, which was proposed recently and achieved b…
Latent Tree Decomposition Parsers for AMR-to-Text Generation
Lisa Jin, Daniel Gildea
Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention. While these approaches apply to general graphs, AMRs may be amen…
AMR-to-Text Generation with Cache Transition Systems
Lisa Jin, Daniel Gildea
Text generation from AMR involves emitting sentences that reflect the meaning of their AMR annotations. Neural sequence-to-sequence models have successfully been used to decode str…
Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea +1
Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue.…
Predicting TED Talk Ratings from Language and Prosody
Md Iftekhar Tanveer, Md Kamrul Hassan, Daniel Gildea +1
We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers. Our dataset contains over 2200 TED Talk transcripts (includes over…
Tree Decomposition Attention for AMR-to-Text Generation
Lisa Jin, Daniel Gildea
Text generation from AMR requires mapping a semantic graph to a string that it annotates. Transformer-based graph encoders, however, poorly capture vertex dependencies that may ben…
SemBleu: A Robust Metric for AMR Parsing Evaluation
Linfeng Song, Daniel Gildea
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nod…
Neural Transition-based Syntactic Linearization
Linfeng Song, Yue Zhang, Daniel Gildea
The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentenc…
Unsupervised Bilingual Lexicon Induction Across Writing Systems
Parker Riley, Daniel Gildea
Recent embedding-based methods in unsupervised bilingual lexicon induction have shown good results, but generally have not leveraged orthographic (spelling) information, which can…
AMR-to-text Generation with Synchronous Node Replacement Grammar
Linfeng Song, Xiaochang Peng, Yue Zhang +2
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic ext…
Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication
Shay B. Cohen, Daniel Gildea
We describe a matrix multiplication recognition algorithm for a subset of binary linear context-free rewriting systems (LCFRS) with running time where …
Exploring phrase-compositionality in skip-gram models
Xiaochang Peng, Daniel Gildea
In this paper, we introduce a variation of the skip-gram model which jointly learns distributed word vector representations and their way of composing to form phrase embeddings. In…
Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients
Iftekhar Naim, Daniel Gildea
The speed of convergence of the Expectation Maximization (EM) algorithm for Gaussian mixture model fitting is known to be dependent on the amount of overlap among the mixture compo…
N-ary Relation Extraction using Graph State LSTM
Linfeng Song, Yue Zhang, Zhiguo Wang +1
Cross-sentence -ary relation extraction detects relations among entities across multiple sentences. Typical methods formulate an input as a \textit{document graph}, integrat…
Synchronous Context-Free Grammars and Optimal Linear Parsing Strategies
Pierluigi Crescenzi, Daniel Gildea, Andrea Marino +2
Synchronous Context-Free Grammars (SCFGs), also known as syntax-directed translation schemata, are unlike context-free grammars in that they do not have a binary normal form. In ge…
Semantic Neural Machine Translation using AMR
Linfeng Song, Daniel Gildea, Yue Zhang +2
It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many…
Sense Embedding Learning for Word Sense Induction
Linfeng Song, Zhiguo Wang, Haitao Mi +1
Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or cooccurrence features, and train a model for each polysemous wo…
Tensors over Semirings for Latent-Variable Weighted Logic Programs
Esma Balkir, Daniel Gildea, Shay Cohen
Semiring parsing is an elegant framework for describing parsers by using semiring weighted logic programs. In this paper we present a generalization of this concept: latent-variabl…
Automated Analysis and Prediction of Job Interview Performance
Iftekhar Naim, M. Iftekhar Tanveer, Daniel Gildea +2
We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing t…
AWLCO: All-Window Length Co-Occurrence
Joshua Sobel, Noah Bertram, Chen Ding +2
Analyzing patterns in a sequence of events has applications in text analysis, computer programming, and genomics research. In this paper, we consider the all-window-length analysis…
AMR-to-text generation as a Traveling Salesman Problem
Linfeng Song, Yue Zhang, Xiaochang Peng +2
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR grap…
Hierarchical Context Tagging for Utterance Rewriting
Lisa Jin, Linfeng Song, Lifeng Jin +2
Utterance rewriting aims to recover coreferences and omitted information from the latest turn of a multi-turn dialogue. Recently, methods that tag rather than linearly generate seq…
Human languages order information efficiently
Daniel Gildea, T. Florian Jaeger
Most languages use the relative order between words to encode meaning relations. Languages differ, however, in what orders they use and how these orders are mapped onto different m…