630 research outputs found
Neural Natural Language Inference Models Enhanced with External Knowledge
Modeling natural language inference is a very challenging task. With the
availability of large annotated data, it has recently become feasible to train
complex models such as neural-network-based inference models, which have shown
to achieve the state-of-the-art performance. Although there exist relatively
large annotated data, can machines learn all knowledge needed to perform
natural language inference (NLI) from these data? If not, how can
neural-network-based NLI models benefit from external knowledge and how to
build NLI models to leverage it? In this paper, we enrich the state-of-the-art
neural natural language inference models with external knowledge. We
demonstrate that the proposed models improve neural NLI models to achieve the
state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201
What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?
Pre-trained models of source code have gained widespread popularity in many
code intelligence tasks. Recently, with the scaling of the model and corpus
size, large language models have shown the ability of in-context learning
(ICL). ICL employs task instructions and a few examples as demonstrations, and
then inputs the demonstrations to the language models for making predictions.
This new learning paradigm is training-free and has shown impressive
performance in various natural language processing and code intelligence tasks.
However, the performance of ICL heavily relies on the quality of
demonstrations, e.g., the selected examples. It is important to systematically
investigate how to construct a good demonstration for code-related tasks. In
this paper, we empirically explore the impact of three key factors on the
performance of ICL in code intelligence tasks: the selection, order, and number
of demonstration examples. We conduct extensive experiments on three code
intelligence tasks including code summarization, bug fixing, and program
synthesis. Our experimental results demonstrate that all the above three
factors dramatically impact the performance of ICL in code intelligence tasks.
Additionally, we summarize our findings and provide takeaway suggestions on how
to construct effective demonstrations, taking into account these three
perspectives. We also show that a carefully-designed demonstration based on our
findings can lead to substantial improvements over widely-used demonstration
construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%,
175.96%, and 50.81% on code summarization, bug fixing, and program synthesis,
respectivelyComment: This paper is accepted by ASE 202
Incorporating Fine-grained Events in Stock Movement Prediction
Considering event structure information has proven helpful in text-based
stock movement prediction. However, existing works mainly adopt the
coarse-grained events, which loses the specific semantic information of diverse
event types. In this work, we propose to incorporate the fine-grained events in
stock movement prediction. Firstly, we propose a professional finance event
dictionary built by domain experts and use it to extract fine-grained events
automatically from finance news. Then we design a neural model to combine
finance news with fine-grained event structure and stock trade data to predict
the stock movement. Besides, in order to improve the generalizability of the
proposed method, we design an advanced model that uses the extracted
fine-grained events as the distant supervised label to train a multi-task
framework of event extraction and stock prediction. The experimental results
show that our method outperforms all the baselines and has good
generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201
Sequence Tagging for Fast Dependency Parsing
[Abstract]
Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word’s dependency relations. We then follow a supervised strategy and train a bidirectional long short-term memory network to learn to predict such linearized trees. Contrary to the previous studies attempting this, the results show that this approach not only leads to accurate but also fast dependency parsing. Furthermore, we obtain even faster and more accurate parsers by recasting the problem as multitask learning, with a twofold objective: to reduce the output vocabulary and also to exploit hidden patterns coming from a second parsing paradigm (constituent grammars) when used as an auxiliary task.Ministerio de EconomĂa y Competitividad; TIN2017-85160-C2-1-RXunta de Galicia; ED431B 2017/0
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Visual modifications to text are often used to obfuscate offensive comments
in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"),
among other scenarios. We consider this as a new type of adversarial attack in
NLP, a setting to which humans are very robust, as our experiments with both
simple and more difficult visual input perturbations demonstrate. We then
investigate the impact of visual adversarial attacks on current NLP systems on
character-, word-, and sentence-level tasks, showing that both neural and
non-neural models are, in contrast to humans, extremely sensitive to such
attacks, suffering performance decreases of up to 82\%. We then explore three
shielding methods---visual character embeddings, adversarial training, and
rule-based recovery---which substantially improve the robustness of the models.
However, the shielding methods still fall behind performances achieved in
non-attack scenarios, which demonstrates the difficulty of dealing with visual
attacks.Comment: Accepted as long paper at NAACL-2019; fixed one ungrammatical
sentenc
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