Source-linked AI summary
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Jiatao Gu, Zhengdong Lu, Hang Li, Victor O. K. Li
TL;DR
Seq2Seq systems struggle to reproduce input segments such as names, dates, and long phrases, motivating an explicit copying mechanism. CopyNet integrates copying with vocabulary generation in an end-to-end differentiable encoder-decoder model, and experiments on synthetic and real datasets demonstrate its efficacy.
Problem
Seq2Seq models may inadequately represent input subsequences such as entity names or dates when the output must refer to them.
Method
CopyNet combines regular vocabulary generation with a copying operation that selects input subsequences and places them in the output, using a differentiable end-to-end encoder-decoder model.
Results
Experiments on synthetic and real-world datasets demonstrate CopyNet’s efficacy, including effective replication of long subsequences and improved handling of out-of-vocabulary words.
Takeaways & Limitations
Copying can complement generation by helping Seq2Seq systems reproduce source segments, including proper nouns and other words unavailable in the fixed vocabulary.
Takeaways & Limitations
The source-specific vocabulary is V ∪ UNK ∪ X, so the copying extension depends on words occurring in the source sequence.
Abstract
from arXiv · showhide
We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or even long phrases in conversation. The challenge with regard to copying in Seq2Seq is that new machinery is needed to decide when to perform the operation. In this paper, we incorporate copying into neural network-based Seq2Seq learning and propose a new model called CopyNet with encoder-decoder structure. CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Our empirical study on both synthetic data sets and real world data sets demonstrates the efficacy of CopyNet. For example, CopyNet can outperform regular RNN-based model with remarkable margins on text summarization tasks.
1 Introduction
Seq2Seq models must handle copied input segments such as entity names and long phrases, which motivates a unified model combining generation with copying.
- 1 Introduction: Copying selectively locates an input segment and places it in the output sequence, as seen with names and longer dialogue phrases.The examples include copying “Chandralekha” and a longer phrase beginning “doesn’t perform exactly.”
- 1 Introduction: Canonical and attention-based Seq2Seq models rely heavily on meaning representations that may be insufficient for reproducing input subsequences such as entity names or dates.
- 1 Introduction: COPYNET unifies ordinary word generation with copying appropriate input segments at suitable output positions.The model is proposed as a neural network-based Seq2Seq approach for accommodating both understanding and rote memorization.
- 1 Introduction: COPYNET is trained end-to-end despite copying being described as a hard operation.The supplied introduction states that the model can be optimized in an end-to-end fashion.
2 Background: Neural Models for Sequence-to-sequence Learning
Neural Seq2Seq learning uses encoder-decoder models to map source sequences to target sequences, while attention dynamically retrieves source information during decoding.
- 2 Background: Neural Models for Sequence-to-sequence Learning: Seq2Seq learning models the target sequence conditioned on the source sequence, commonly through an encoder-decoder architecture.
- 2.1 RNN Encoder-Decoder: The encoder converts the source sequence into a fixed-length context vector, which the decoder unfolds into the target sequence.The decoder predicts each target symbol using its recurrent state and previously generated symbols.
- 2.1 RNN Encoder-Decoder: The decoder prediction model is typically a classifier over a vocabulary of approximately 30,000 words.
- 2.2 The Attention Mechanism: Attention reduces the burden of compressing the entire source into one fixed-length vector by using a dynamically changing context during decoding.Soft attention represents the context as a weighted sum of source hidden states.
3 COPYNET
COPYNET extends the encoder-decoder architecture with a differentiable copying mechanism that combines vocabulary generation with source-word copying. Its decoder uses source representations, selective reads, and hybrid content- and location-based addressing to reproduce input subsequences and handle source-side OOV words.
- Model overview: The source encoder is a bidirectional RNN whose location-aware hidden states form short-term memory M for decoding.Each hidden state corresponds to a source word and can be accessed repeatedly during target generation.
- Model overview: COPYNET combines generate-mode word prediction with copy-mode selection from the source sequence in an encoder-decoder architecture.The copy mode selects words from the source, while the encoder supplies a sequence of hidden states used as memory.
- State update: COPYNET updates decoder states with both word embeddings and location-specific source representations, unlike canonical attention-based decoders.The previous target symbol is represented using its embedding together with a selective read from M.
- Hybrid addressing: Selective read provides a higher-precision source lookup that can help the decoder cover consecutive copied subsequences.When the previous symbol appears in the source, the selective read concentrates on its source locations and can support moving to the next position.
- Hybrid addressing: COPYNET combines semantic attentive reading with location-based selective reading, while the decoder learns when to enter and leave copy-mode.This hybrid strategy coordinates content-based and location-based addressing without explicitly fixing the addressing details for every task.
- Handling out-of-vocabulary words: Copying extends the instance-specific vocabulary with source words, enabling COPYNET to reproduce OOV words and long OOV segments.The paper reports that this alleviates the OOV problem, especially for target-side proper nouns that replicate source-side words.
4 Learning
COPYNET trains its copying and generation mechanisms jointly by maximizing the likelihood of target sequences. Because the two modes form a shared probabilistic model, the network learns their coordination without additional mode labels.
- Learning: COPYNET minimizes the negative log-likelihood over batches of source and target sequences.The objective is defined directly over the observed training sequences.
- Learning: The target-word probability is modeled as a mixture of generate-mode and copy-mode, eliminating the need for separate mode labels.The network learns to coordinate the two modes from data.
- Learning: When a target word occurs in the source, gradients encourage copy-mode; otherwise, shared normalization discourages it, and one mode usually dominates.The two modes compete through the shared normalization term Z.
5 Experiments
COPYNET was evaluated on synthetic transformation, text summarization, and single-turn dialogue tasks. Across these settings, it learned to copy input subsequences, handle OOV words, and coordinate copied and generated content.
- 5.1 Synthetic Dataset: COPYNET significantly outperformed Enc-Dec and RNNsearch on synthetic rule types requiring variable subsequence replication, except “x →∅”.The results indicate accurate placement of relatively long copied symbol sequences, whereas attention alone remained inadequate for strict replication.
- 5.1 Synthetic Dataset: COPYNET’s decoder becomes copy-dominated while traversing a subsequence to replicate, then switches to generate-mode after leaving it.This behavior demonstrates precise coordination between copying and ordinary word generation.
- 5.2 Text Summarization: On LCSTS, COPYNET beat the competitor models by a big margin in text summarization, including character- and word-based variants.The evaluation used ROUGE-1, ROUGE-2, and ROUGE-L, with COPYNET using substantially smaller vocabularies than the compared models.
- 5.2.1 Case Study: COPYNET summaries were usually fluent despite relying mostly on copied words, combining consecutive source segments with content-based and location-based addressing.The model also generated connective words, giving its summaries both extractive and abstractive characteristics.
- 5.2.1 Case Study: COPYNET handled OOV words well in summarization, while canonical RNN approaches often failed on documents and summaries containing many OOVs.The copying mechanism can extend the effective vocabulary to include source words, including entity names and other unseen tokens.
- 5.3 Single-turn Dialogue: In single-turn dialogue, COPYNET significantly outperformed RNNsearch, especially on DS-II, where filled substrings did not overlap between training and testing.RNNsearch performance dropped substantially on unseen words, whereas COPYNET’s performance dropped only slightly by copying slot values from the input.
6 Related Work
COPYNET is positioned relative to pointer, OOV-handling, information-carrying, and extraction-based approaches. Its distinguishing feature is combining copying with ordinary word generation in a broader Seq2Seq model.
- Unlike Pointer Networks, COPYNET can generate words outside the input sequence while also copying from it.Pointer Networks predict directly from the input and cannot predict outside the input set.
- COPYNET addresses out-of-vocabulary words end-to-end rather than relying on source annotations and postprocessing heuristics.The paper contrasts its systemic approach with heuristic postprocessing for neural machine translation.
- The copying mechanism carries information forward without nonlinear transformation, paralleling shortcut connections proposed for very deep networks.
- Compared with related extraction and pointing methods, COPYNET is described as more general and not limited to one task or OOV words.The comparison includes summarization and machine-translation approaches that extract or point to source content.
7 Conclusion and Future Work
The paper concludes that COPYNET incorporates copying into Seq2Seq learning and identifies heterogeneous source-target tasks as a direction for future work.
- COPYNET incorporates copying into the sequence-to-sequence learning framework.
- Future work will extend copying to tasks whose source and target have heterogeneous types.
- Machine translation is given as an example of a future heterogeneous source-target application.