Xiaochang Peng


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UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
Aaron Chan | Maziar Sanjabi | Lambert Mathias | Liang Tan | Shaoliang Nie | Xiaochang Peng | Xiang Ren | Hamed Firooz
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

An extractive rationale explains a language model’s (LM’s) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale extraction should be faithful (reflective of LM’s actual behavior) and plausible (convincing to humans), without compromising the LM’s (i.e., task model’s) task performance. Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata. In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (i.e., faithfulness and plausibility criteria); and (3) jointly the train task model and rationale extractor on the task using selected objectives. UNIREX enables replacing prior works’ heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3). To facilitate comparison between methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric. Across five English text classification datasets, our best UNIREX configuration outperforms the strongest baselines by an average of 32.9% NRG. Plus, we find that UNIREX-trained rationale extractors’ faithfulness can even generalize to unseen datasets and tasks.


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Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Fan Yang | Xiaochang Peng | Gargi Ghosh | Reshef Shilon | Hao Ma | Eider Moore | Goran Predovic
Proceedings of the Third Workshop on Abusive Language Online

Interactions among users on social network platforms are usually positive, constructive and insightful. However, sometimes people also get exposed to objectionable content such as hate speech, bullying, and verbal abuse etc. Most social platforms have explicit policy against hate speech because it creates an environment of intimidation and exclusion, and in some cases may promote real-world violence. As users’ interactions on today’s social networks involve multiple modalities, such as texts, images and videos, in this paper we explore the challenge of automatically identifying hate speech with deep multimodal technologies, extending previous research which mostly focuses on the text signal alone. We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.

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Neural Models of Text Normalization for Speech Applications
Hao Zhang | Richard Sproat | Axel H. Ng | Felix Stahlberg | Xiaochang Peng | Kyle Gorman | Brian Roark
Computational Linguistics, Volume 45, Issue 2 - June 2019

Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS). In this application, one must decide, for example, that 123 is verbalized as one hundred twenty three in 123 pages but as one twenty three in 123 King Ave. For this task, state-of-the-art industrial systems depend heavily on hand-written language-specific grammars.We propose neural network models that treat text normalization for TTS as a sequence-to-sequence problem, in which the input is a text token in context, and the output is the verbalization of that token. We find that the most effective model, in accuracy and efficiency, is one where the sentential context is computed once and the results of that computation are combined with the computation of each token in sequence to compute the verbalization. This model allows for a great deal of flexibility in terms of representing the context, and also allows us to integrate tagging and segmentation into the process.These models perform very well overall, but occasionally they will predict wildly inappropriate verbalizations, such as reading 3 cm as three kilometers. Although rare, such verbalizations are a major issue for TTS applications. We thus use finite-state covering grammars to guide the neural models, either during training and decoding, or just during decoding, away from such “unrecoverable” errors. Such grammars can largely be learned from data.

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Ordered Tree Decomposition for HRG Rule Extraction
Daniel Gildea | Giorgio Satta | Xiaochang Peng
Computational Linguistics, Volume 45, Issue 2 - June 2019

We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. 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. The assumption of a fixed order for the vertices of the input graph makes it possible to solve the problem in polynomial time, in contrast to the fact that the problem of finding optimal tree decompositions for a graph is NP-hard. We also present polynomial-time algorithms for parsing based on our HRGs, where the input is a vertex sequence and the output is a graph structure. The intended application of our algorithms is grammar extraction and parsing for semantic representation of natural language. We apply our algorithms to data annotated with Abstract Meaning Representations and report on the characteristics of the resulting grammars.


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Sequence-to-sequence Models for Cache Transition Systems
Xiaochang Peng | Linfeng Song | Daniel Gildea | Giorgio Satta
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs. We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system. To address the sparsity issue of neural AMR parsing, we feed feature embeddings from the transition state to provide relevant local information for each decoder state. We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus. We evaluate our neural transition model on the AMR parsing task, and our parser outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models.

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Cache Transition Systems for Graph Parsing
Daniel Gildea | Giorgio Satta | Xiaochang Peng
Computational Linguistics, Volume 44, Issue 1 - April 2018

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. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora.


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Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng | Chuan Wang | Daniel Gildea | Nianwen Xue
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.

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AMR-to-text Generation with Synchronous Node Replacement Grammar
Linfeng Song | Xiaochang Peng | Yue Zhang | Zhiguo Wang | Daniel Gildea
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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 extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.


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AMR-to-text generation as a Traveling Salesman Problem
Linfeng Song | Yue Zhang | Xiaochang Peng | Zhiguo Wang | Daniel Gildea
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
Xiaochang Peng | Daniel Gildea
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing
Xiaochang Peng | Linfeng Song | Daniel Gildea
Proceedings of the Nineteenth Conference on Computational Natural Language Learning


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Type-based MCMC for Sampling Tree Fragments from Forests
Xiaochang Peng | Daniel Gildea
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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Capturing Long-distance Dependencies in Sequence Models: A Case Study of Chinese Part-of-speech Tagging
Weiwei Sun | Xiaochang Peng | Xiaojun Wan
Proceedings of the Sixth International Joint Conference on Natural Language Processing