Source-linked AI summary
Bilateral Multi-Perspective Matching for Natural Language Sentences
Zhiguo Wang, Wael Hamza, Radu Florian
TL;DR
Natural language sentence matching must identify relationships between sentence pairs across several tasks, while prior methods were limited by matching direction or granularity. BiMPM uses bilateral, multi-perspective matching with aggregation, and achieves state-of-the-art performance on paraphrase identification, natural language inference, and answer sentence selection.
Problem
Natural language sentence matching supports multiple tasks, but previous approaches were limited by single-direction or single-granularity matching.
Method
BiMPM encodes sentence pairs with BiLSTMs, matches them bidirectionally from multiple perspectives, aggregates the results, and predicts from a fixed-length matching vector.
Results
BiMPM achieves state-of-the-art performance on paraphrase identification, natural language inference, and answer sentence selection.
Takeaways & Limitations
The model provides a matching-aggregation approach that combines bilateral and multi-perspective comparisons across three NLSM tasks.
Takeaways & Limitations
The authors did not experiment with more perspectives because of practical limitations.
Abstract
from arXiv · showhide
Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the "matching-aggregation" framework. Given two sentences $P$ and $Q$, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions $P \rightarrow Q$ and $P \leftarrow Q$. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.
1 Introduction
Natural language sentence matching compares sentences to identify their relationship and supports tasks including paraphrase identification, inference, and answer selection. BiMPM addresses limitations in prior matching approaches through bilateral, multi-perspective matching and achieves state-of-the-art performance across three evaluated tasks.
- Natural language sentence matching compares two sentences and identifies their relationship for applications including paraphrase identification, inference, and answer selection.It is also used in machine comprehension to match passages with questions and identify answer spans.
- Prior NLSM approaches either used Siamese sentence encoders or limited matching to a single granularity or direction.The Siamese framework independently encodes both sentences into sentence vectors before making a matching decision.
- BiMPM encodes both sentences, matches them in both directions from multiple perspectives, aggregates the results with another BiLSTM, and predicts through a fully connected layer.The model is proposed under the matching-aggregation framework.
- The paper evaluates BiMPM on paraphrase identification, natural language inference, and answer sentence selection using standard benchmark datasets.The paper describes evaluation and related-work organization across these tasks.
- BiMPM achieves state-of-the-art performance on all three evaluated tasks.
2 Task Definition
The NLSM task models each example as two sentences and a relationship label, then estimates the label distribution conditioned on the sentence pair. The label meanings depend on the application, such as paraphrase identification, inference, or answer selection.
- Each NLSM example is represented as a triple (P, Q, y), where P and Q are sentences and y is their relationship label.The task estimates Pr(y|P, Q) and predicts the label with the highest conditional probability.
- In paraphrase identification, y indicates whether P and Q are paraphrases of each other.The labels are y = 1 for paraphrases and y = 0 otherwise.
- In natural language inference, P is a premise and Q is a hypothesis labeled entailment, contradiction, or neutral.Entailment means Q can be inferred from P; contradiction means Q cannot be true given P; neutral means they are irrelevant to each other.
- In answer sentence selection, P is a question and Q is a candidate answer labeled according to whether Q is correct for P.The positive label indicates a correct answer, while the negative label indicates an incorrect answer.
3 Method
BiMPM encodes both sentences, matches them bidirectionally using multiple matching perspectives, aggregates the results into a fixed-length vector, and predicts the sentence relationship.
- Model Overview: BiMPM uses a matching-aggregation architecture with contextual BiLSTM encodings, bilateral matching, matching-result aggregation, and prediction layers.The model estimates Pr(y|P, Q) through five layers, including word representation, context representation, matching, aggregation, and prediction.
- Model Overview: The matching layer compares every time-step of each sentence against all time-steps of the other sentence in both directions.It produces two sequences of matching vectors, with each vector representing one time-step’s matching results against the other sentence.
- Multi-perspective Matching Operation: The multi-perspective function returns an l-dimensional matching vector whose values use cosine similarity between dimension-weighted input vectors.Each perspective applies its own trainable weights to the dimensions before cosine comparison.
- Multi-perspective Matching Operation: Four strategies compare time-steps across sentences: Full-Matching, Maxpooling-Matching, Attentive-Matching, and Max-Attentive-Matching.These strategies respectively use endpoint comparisons, element-wise maxima, weighted attentive vectors, and the highest-similarity contextual embedding.
- Multi-perspective Matching Operation: The four strategies generate eight vectors per time-step, and reverse-direction processing applies the same procedure to the other sentence.The resulting matching sequences are independently processed by another BiLSTM and compressed into a fixed-length matching vector.
4 Experiments
Experiments evaluate BiMPM on paraphrase identification, natural language inference, and answer sentence selection, alongside ablations and baseline comparisons. The results support multi-perspective and bilateral matching, with state-of-the-art or competitive performance across the evaluated tasks.
- Experimental Setup: BiMPM is evaluated on paraphrase identification, natural language inference, and answer sentence selection using standard benchmark datasets.The experiments include Quora Question Pairs, SNLI, TREC-QA, and WikiQA.
- Model Properties: Increasing the number of multi-perspective cosine matching views improves development-set performance, while even one perspective significantly outperforms vanilla cosine matching.The tested perspective counts are l ∈ {1, 5, 10, 15, 20}; l = 0 denotes the baseline.
- Model Properties: About 1 percent: restricting matching to a single direction hurts development-set performance compared with the full bilateral model.The ablations compare “Only P →Q” and “Only P ←Q” against the “Full Model.”
- Model Properties: Eliminating any of the four matching strategies significantly hurts development-set performance.The ablations remove Full-Matching, Maxpooling-Matching, Attentive-Matching, or Max-Attentive-Matching individually.
- Paraphrase Identification: More than two percent: BiMPM outperforms the L.D.C. model on paraphrase identification, while multi-perspective CNN and LSTM baselines outperform their Siamese counterparts.These comparisons indicate effectiveness of the multi-perspective cosine matching function and the overall model for paraphrase identification.
- Natural Language Inference and Answer Sentence Selection: On SNLI, BiMPM achieves state-of-the-art performance in both single-model and ensemble scenarios, while answer-sentence-selection performance is on par with state-of-the-art models.The ensemble averages probability distributions from four BiMPM models initialized with different seeds.
5 Related Work
Natural language sentence matching evolved from hand-crafted features and Siamese sentence-vector models toward multi-granular interaction frameworks. These approaches improve generalization across tasks by modeling lower-level interactions between sentences.
- Hand-crafted NLSM features capture n-gram overlap, word reordering, and syntactic alignment but are difficult to generalize across tasks and datasets.
- Siamese models independently encode both sentences into vectors and make the matching decision solely from those sentence representations.Shared parameters make these models smaller and easier to train.
- Siamese sentence-vector models ignore lower-level interactive features between the two sentences.
- For answer sentence selection, this work uses the clean version of the TREC-QA dataset and compares only with methods reporting results on that version.
- Multi-granular matching frameworks were proposed to capture interactions between sentences at multiple levels, and experiments reported better performance than earlier methods across tasks.
6 Conclusion
The paper proposes BiMPM, a matching-aggregation model that matches sentence pairs bidirectionally and from multiple perspectives. It evaluates the model on three sentence-matching tasks and reports state-of-the-art performance on all of them.
- BiMPM matches sentences P and Q in both directions, P →Q and P ←Q, rather than using a single matching direction.
- Within each direction, BiMPM applies multiple matching perspectives to the two sentences.
- The model is evaluated on paraphrase identification, natural language inference, and answer sentence selection.
- BiMPM achieves state-of-the-art performance on all three evaluated tasks.