Search Results for author:
Found 58 papers, 9 papers with code
Sequence-to-sequence AMR Parsing with Ancestor Information
AMR parsing is the task that maps a sentence to an AMR semantic graph automatically.
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation
A common way to combat exposure bias is by applying scores from evaluation metrics as rewards in reinforcement learning (RL).
Efficient Outside Computation
Weighted deduction systems provide a framework for describing parsing algorithms that can be used with a variety of operations for combining the values of partial derivations.
Strictly Breadth-First AMR Parsing
AMR parsing is the task that maps a sentence to an AMR semantic graph automatically.
Hierarchical Context Tagging for Utterance Rewriting
HCT (i) tags the source string with token-level edit actions and slotted rules and (ii) fills in the resulting rule slots with spans from the dialogue context.
Latent Tree Decomposition Parsers for AMR-to-Text Generation
Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention.
Tree Decomposition Attention for AMR-to-Text Generation
Text generation from AMR requires mapping a semantic graph to a string that it annotates.
Outside Computation with Superior Functions
We show that a general algorithm for efficient computation of outside values under the minimum of superior functions framework proposed by Knuth (1977) would yield a sub-exponential time algorithm for SAT, violating the Strong Exponential Time Hypothesis (SETH).
Generalized Shortest-Paths Encoders for AMR-to-Text Generation
Instead of feeding shortest paths to the vertex self-attention module, we train a model to learn them using generalized shortest-paths algorithms.
Tensors over Semirings for Latent-Variable Weighted Logic Programs
Semiring parsing is an elegant framework for describing parsers by using semiring weighted logic programs.
Unsupervised Bilingual Lexicon Induction Across Writing Systems
Recent embedding-based methods in unsupervised bilingual lexicon induction have shown good results, but generally have not leveraged orthographic (spelling) information, which can be helpful for pairs of related languages.
AMR-to-Text Generation with Cache Transition Systems
To enforce a sentence-aligned graph traversal and provide local graph context, we predict transition-based parser actions in addition to English words.
Leveraging Dependency Forest for Neural Medical Relation Extraction
Medical relation extraction discovers relations between entity mentions in text, such as research articles.
Ordered Tree Decomposition for HRG Rule Extraction
Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure.
SemBleu: A Robust Metric for AMR Parsing Evaluation
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs.
Ranked #3 on
Graph Matching
on RARE
A Causality-Guided Prediction of the TED Talk Ratings from the Speech-Transcripts using Neural Networks
Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills.
Predicting TED Talk Ratings from Language and Prosody
We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers.
Semantic Neural Machine Translation using AMR
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 sentences correspond to one meaning) of machine translation models.
Neural Transition-based Syntactic Linearization
The task of linearization is to find a grammatical order given a set of words.
N-ary Relation Extraction using Graph-State LSTM
Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences.
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
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.
Ranked #2 on
Question Answering
on COMPLEXQUESTIONS
Feature-Based Decipherment for Machine Translation
The existing decipherment models, however, are not well suited for exploiting these orthographic similarities.
N-ary Relation Extraction using Graph State LSTM
Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences.
Sequence-to-sequence Models for Cache Transition Systems
In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs.
Orthographic Features for Bilingual Lexicon Induction
Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages.
Leveraging Context Information for Natural Question Generation
The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer.
Ranked #11 on
Question Generation
on SQuAD1.1
A Graph-to-Sequence Model for AMR-to-Text Generation
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.
Ranked #1 on
Graph-to-Sequence
on LDC2015E86:
(using extra training data)
Weighted DAG Automata for Semantic Graphs
Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning.
A Notion of Semantic Coherence for Underspecified Semantic Representation
The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete.
Cache Transition Systems for Graph Parsing
Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree.
Addressing the Data Sparsity Issue in Neural AMR Parsing
Neural attention models have achieved great success in different NLP tasks.
AMR-to-text Generation with Synchronous Node Replacement Grammar
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar.
AMR-to-text generation as a Traveling Salesman Problem
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph.
Exploring phrase-compositionality in skip-gram models
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.
Sense Embedding Learning for Word Sense Induction
In the training stage, our method induces several sense centroids (embedding) for each polysemous word.
Ranked #4 on
Word Sense Induction
on SemEval 2010 WSI
Human languages order information efficiently
Most languages use the relative order between words to encode meaning relations.
Feature-based Decipherment for Large Vocabulary Machine Translation
Our results show that the proposed log-linear model with contrastive divergence scales to large vocabularies and outperforms the existing generative decipherment models by exploiting the orthographic features.
Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication
Our result provides another proof for the best known result for parsing mildly context sensitive formalisms such as combinatory categorial grammars, head grammars, linear indexed grammars, and tree adjoining grammars, which can be parsed in time $O(n^{4. 76})$.
Automated Analysis and Prediction of Job Interview Performance
We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews.
Synchronous Context-Free Grammars and Optimal Linear Parsing Strategies
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.
